├── .gitignore ├── CITATION.cff ├── LICENSE ├── README.md ├── SimulateGPT.ipynb ├── SimulateGPT_graphical_abstract.jpg ├── Snakefile ├── config.yaml ├── env.yml ├── experiments ├── crc │ ├── .gitignore │ ├── ai_messages │ │ ├── baseline--crc_apc_impact_2020_0 │ │ ├── baseline--crc_apc_impact_2020_1 │ │ ├── baseline--crc_apc_impact_2020_10 │ │ ├── baseline--crc_apc_impact_2020_11 │ │ ├── baseline--crc_apc_impact_2020_12 │ │ ├── baseline--crc_apc_impact_2020_13 │ │ ├── baseline--crc_apc_impact_2020_14 │ │ ├── baseline--crc_apc_impact_2020_15 │ │ ├── baseline--crc_apc_impact_2020_16 │ │ ├── baseline--crc_apc_impact_2020_17 │ │ ├── baseline--crc_apc_impact_2020_18 │ │ ├── baseline--crc_apc_impact_2020_19 │ │ ├── baseline--crc_apc_impact_2020_20 │ │ ├── baseline--crc_apc_impact_2020_3 │ │ ├── baseline--crc_apc_impact_2020_5 │ │ ├── baseline--crc_apc_impact_2020_6 │ │ ├── baseline--crc_apc_impact_2020_8 │ │ ├── baseline--crc_apc_impact_2020_9 │ │ ├── high5step_complexity--crc_apc_impact_2020_0 │ │ ├── high5step_complexity--crc_apc_impact_2020_1 │ │ ├── high5step_complexity--crc_apc_impact_2020_10 │ │ ├── high5step_complexity--crc_apc_impact_2020_11 │ │ ├── high5step_complexity--crc_apc_impact_2020_12 │ │ ├── high5step_complexity--crc_apc_impact_2020_13 │ │ ├── high5step_complexity--crc_apc_impact_2020_14 │ │ ├── high5step_complexity--crc_apc_impact_2020_15 │ │ ├── high5step_complexity--crc_apc_impact_2020_16 │ │ ├── high5step_complexity--crc_apc_impact_2020_17 │ │ ├── high5step_complexity--crc_apc_impact_2020_18 │ │ ├── high5step_complexity--crc_apc_impact_2020_19 │ │ ├── high5step_complexity--crc_apc_impact_2020_20 │ │ ├── high5step_complexity--crc_apc_impact_2020_3 │ │ ├── high5step_complexity--crc_apc_impact_2020_5 │ │ ├── high5step_complexity--crc_apc_impact_2020_6 │ │ ├── high5step_complexity--crc_apc_impact_2020_8 │ │ ├── high5step_complexity--crc_apc_impact_2020_9 │ │ ├── high_complexity--crc_apc_impact_2020_0 │ │ ├── high_complexity--crc_apc_impact_2020_1 │ │ ├── high_complexity--crc_apc_impact_2020_10 │ │ ├── high_complexity--crc_apc_impact_2020_11 │ │ ├── high_complexity--crc_apc_impact_2020_12 │ │ ├── high_complexity--crc_apc_impact_2020_13 │ │ ├── high_complexity--crc_apc_impact_2020_14 │ │ ├── high_complexity--crc_apc_impact_2020_15 │ │ ├── high_complexity--crc_apc_impact_2020_16 │ │ ├── high_complexity--crc_apc_impact_2020_17 │ │ ├── high_complexity--crc_apc_impact_2020_18 │ │ ├── high_complexity--crc_apc_impact_2020_19 │ │ ├── high_complexity--crc_apc_impact_2020_20 │ │ ├── high_complexity--crc_apc_impact_2020_3 │ │ ├── high_complexity--crc_apc_impact_2020_5 │ │ ├── high_complexity--crc_apc_impact_2020_6 │ │ ├── high_complexity--crc_apc_impact_2020_8 │ │ ├── high_complexity--crc_apc_impact_2020_9 │ │ ├── low_complexity--crc_apc_impact_2020_0 │ │ ├── low_complexity--crc_apc_impact_2020_1 │ │ ├── low_complexity--crc_apc_impact_2020_10 │ │ ├── low_complexity--crc_apc_impact_2020_11 │ │ ├── low_complexity--crc_apc_impact_2020_12 │ │ ├── low_complexity--crc_apc_impact_2020_13 │ │ ├── low_complexity--crc_apc_impact_2020_14 │ │ ├── low_complexity--crc_apc_impact_2020_15 │ │ ├── low_complexity--crc_apc_impact_2020_16 │ │ ├── low_complexity--crc_apc_impact_2020_17 │ │ ├── low_complexity--crc_apc_impact_2020_18 │ │ ├── low_complexity--crc_apc_impact_2020_19 │ │ ├── low_complexity--crc_apc_impact_2020_20 │ │ ├── low_complexity--crc_apc_impact_2020_3 │ │ ├── low_complexity--crc_apc_impact_2020_5 │ │ ├── low_complexity--crc_apc_impact_2020_6 │ │ ├── low_complexity--crc_apc_impact_2020_8 │ │ └── low_complexity--crc_apc_impact_2020_9 │ ├── all_data_points.csv │ ├── analysis.py │ ├── crc_apc_impact_2020.csv │ ├── main.md │ ├── plots │ │ ├── CRC_baseline_high_raw.pdf │ │ ├── CRC_high_complexity__baseline.pdf │ │ ├── CRC_high_complexity__baseline_edit1.pdf │ │ ├── CRC_high_complexity__high5step_complexity.pdf │ │ ├── CRC_high_complexity__high5step_complexity_edit1.pdf │ │ ├── CRC_high_complexity__low_complexity.pdf │ │ ├── CRC_high_complexity__low_complexity_edit1.pdf │ │ ├── all_in_one_reg.pdf │ │ ├── all_in_one_reg.png │ │ ├── baseline_vs_high_reg.pdf │ │ ├── baseline_vs_high_reg.png │ │ ├── pfs_regression_plot.pdf │ │ ├── pfs_regression_plot.png │ │ ├── subplots_reg.pdf │ │ └── subplots_reg.png │ ├── prompt_generation.py │ ├── prompt_template │ ├── prompts │ │ ├── crc_apc_impact_2020_0 │ │ ├── crc_apc_impact_2020_1 │ │ ├── crc_apc_impact_2020_10 │ │ ├── crc_apc_impact_2020_11 │ │ ├── crc_apc_impact_2020_12 │ │ ├── crc_apc_impact_2020_13 │ │ ├── crc_apc_impact_2020_14 │ │ ├── crc_apc_impact_2020_15 │ │ ├── crc_apc_impact_2020_16 │ │ ├── crc_apc_impact_2020_17 │ │ ├── crc_apc_impact_2020_18 │ │ ├── crc_apc_impact_2020_19 │ │ ├── crc_apc_impact_2020_20 │ │ ├── crc_apc_impact_2020_3 │ │ ├── crc_apc_impact_2020_5 │ │ ├── crc_apc_impact_2020_6 │ │ ├── crc_apc_impact_2020_8 │ │ └── crc_apc_impact_2020_9 │ ├── result_metrics.csv │ └── scatterplots_crc.R ├── gene_essentiality_cancer │ ├── ai_messages │ │ ├── baseline--AGAP5 │ │ ├── baseline--ANKLE2 │ │ ├── baseline--AQP8 │ │ ├── baseline--CCT5 │ │ ├── baseline--DCTN3 │ │ ├── baseline--DHX16 │ │ ├── baseline--DSPP │ │ ├── baseline--EIF2B3 │ │ ├── baseline--FAM71C │ │ ├── baseline--GK2 │ │ ├── baseline--GPR78 │ │ ├── baseline--GTF3C1 │ │ ├── baseline--H2BC1 │ │ ├── baseline--HAUS4 │ │ ├── baseline--HNRNPK │ │ ├── baseline--HNRNPL │ │ ├── baseline--HSPD1 │ │ ├── baseline--IFNA10 │ │ ├── baseline--IL12B │ │ ├── baseline--IL17F │ │ ├── baseline--KIF2B │ │ ├── baseline--KRT25 │ │ ├── baseline--KRT26 │ │ ├── baseline--KRT82 │ │ ├── baseline--KRTAP9-3 │ │ ├── baseline--LGALS14 │ │ ├── baseline--MRGPRX2 │ │ ├── baseline--NDST4 │ │ ├── baseline--NDUFB3 │ │ ├── baseline--NOC3L │ │ ├── baseline--OPA1 │ │ ├── baseline--OR3A3 │ │ ├── baseline--OR52W1 │ │ ├── baseline--OR5T2 │ │ ├── baseline--POLR3H │ │ ├── baseline--PRPF19 │ │ ├── baseline--PSMA7 │ │ ├── baseline--PSMB7 │ │ ├── baseline--RBX1 │ │ ├── baseline--RPL27 │ │ ├── baseline--RPL37A │ │ ├── baseline--RPL7A │ │ ├── baseline--S100A7A │ │ ├── baseline--SDR9C7 │ │ ├── baseline--SLC25A2 │ │ ├── baseline--SS18L2 │ │ ├── baseline--SUPT16H │ │ ├── baseline--TIMM29 │ │ ├── baseline--TMPRSS11F │ │ ├── baseline--ZP2 │ │ ├── high_complexity--AGAP5 │ │ ├── high_complexity--ANKLE2 │ │ ├── high_complexity--AQP8 │ │ ├── high_complexity--CCT5 │ │ ├── high_complexity--DCTN3 │ │ ├── high_complexity--DHX16 │ │ ├── high_complexity--DSPP │ │ ├── high_complexity--EIF2B3 │ │ ├── high_complexity--FAM71C │ │ ├── high_complexity--GK2 │ │ ├── high_complexity--GPR78 │ │ ├── high_complexity--GTF3C1 │ │ ├── high_complexity--H2BC1 │ │ ├── high_complexity--HAUS4 │ │ ├── high_complexity--HNRNPK │ │ ├── high_complexity--HNRNPL │ │ ├── high_complexity--HSPD1 │ │ ├── high_complexity--IFNA10 │ │ ├── high_complexity--IL12B │ │ ├── high_complexity--IL17F │ │ ├── high_complexity--KIF2B │ │ ├── high_complexity--KRT25 │ │ ├── high_complexity--KRT26 │ │ ├── high_complexity--KRT82 │ │ ├── high_complexity--KRTAP9-3 │ │ ├── high_complexity--LGALS14 │ │ ├── high_complexity--MRGPRX2 │ │ ├── high_complexity--NDST4 │ │ ├── high_complexity--NDUFB3 │ │ ├── high_complexity--NOC3L │ │ ├── high_complexity--OPA1 │ │ ├── high_complexity--OR3A3 │ │ ├── high_complexity--OR52W1 │ │ ├── high_complexity--OR5T2 │ │ ├── high_complexity--POLR3H │ │ ├── high_complexity--PRPF19 │ │ ├── high_complexity--PSMA7 │ │ ├── high_complexity--PSMB7 │ │ ├── high_complexity--RBX1 │ │ ├── high_complexity--RPL27 │ │ ├── high_complexity--RPL37A │ │ ├── high_complexity--RPL7A │ │ ├── high_complexity--S100A7A │ │ ├── high_complexity--SDR9C7 │ │ ├── high_complexity--SLC25A2 │ │ ├── high_complexity--SS18L2 │ │ ├── high_complexity--SUPT16H │ │ ├── high_complexity--TIMM29 │ │ ├── high_complexity--TMPRSS11F │ │ ├── high_complexity--ZP2 │ │ ├── low_complexity--AGAP5 │ │ ├── low_complexity--ANKLE2 │ │ ├── low_complexity--AQP8 │ │ ├── low_complexity--CCT5 │ │ ├── low_complexity--DCTN3 │ │ ├── low_complexity--DHX16 │ │ ├── low_complexity--DSPP │ │ ├── low_complexity--EIF2B3 │ │ ├── low_complexity--FAM71C │ │ ├── low_complexity--GK2 │ │ ├── low_complexity--GPR78 │ │ ├── low_complexity--GTF3C1 │ │ ├── low_complexity--H2BC1 │ │ ├── low_complexity--HAUS4 │ │ ├── low_complexity--HNRNPK │ │ ├── low_complexity--HNRNPL │ │ ├── low_complexity--HSPD1 │ │ ├── low_complexity--IFNA10 │ │ ├── low_complexity--IL12B │ │ ├── low_complexity--IL17F │ │ ├── low_complexity--KIF2B │ │ ├── low_complexity--KRT25 │ │ ├── low_complexity--KRT26 │ │ ├── low_complexity--KRT82 │ │ ├── low_complexity--KRTAP9-3 │ │ ├── low_complexity--LGALS14 │ │ ├── low_complexity--MRGPRX2 │ │ ├── low_complexity--NDST4 │ │ ├── low_complexity--NDUFB3 │ │ ├── low_complexity--NOC3L │ │ ├── low_complexity--OPA1 │ │ ├── low_complexity--OR3A3 │ │ ├── low_complexity--OR52W1 │ │ ├── low_complexity--OR5T2 │ │ ├── low_complexity--POLR3H │ │ ├── low_complexity--PRPF19 │ │ ├── low_complexity--PSMA7 │ │ ├── low_complexity--PSMB7 │ │ ├── low_complexity--RBX1 │ │ ├── low_complexity--RPL27 │ │ ├── low_complexity--RPL37A │ │ ├── low_complexity--RPL7A │ │ ├── low_complexity--S100A7A │ │ ├── low_complexity--SDR9C7 │ │ ├── low_complexity--SLC25A2 │ │ ├── low_complexity--SS18L2 │ │ ├── low_complexity--SUPT16H │ │ ├── low_complexity--TIMM29 │ │ ├── low_complexity--TMPRSS11F │ │ └── low_complexity--ZP2 │ ├── data │ │ └── gene_essentiality_cancer │ │ │ ├── AchillesNonessentialControls.csv │ │ │ └── CRISPRInferredCommonEssentials.csv │ ├── main.md │ ├── prompts │ │ ├── AGAP5 │ │ ├── ANKLE2 │ │ ├── AQP8 │ │ ├── CCT5 │ │ ├── DCTN3 │ │ ├── DHX16 │ │ ├── DSPP │ │ ├── EIF2B3 │ │ ├── FAM71C │ │ ├── GK2 │ │ ├── GPR78 │ │ ├── GTF3C1 │ │ ├── H2BC1 │ │ ├── HAUS4 │ │ ├── HNRNPK │ │ ├── HNRNPL │ │ ├── HSPD1 │ │ ├── IFNA10 │ │ ├── IL12B │ │ ├── IL17F │ │ ├── KIF2B │ │ ├── KRT25 │ │ ├── KRT26 │ │ ├── KRT82 │ │ ├── KRTAP9-3 │ │ ├── LGALS14 │ │ ├── MRGPRX2 │ │ ├── NDST4 │ │ ├── NDUFB3 │ │ ├── NOC3L │ │ ├── OPA1 │ │ ├── OR3A3 │ │ ├── OR52W1 │ │ ├── OR5T2 │ │ ├── POLR3H │ │ ├── PRPF19 │ │ ├── PSMA7 │ │ ├── PSMB7 │ │ ├── RBX1 │ │ ├── RPL27 │ │ ├── RPL37A │ │ ├── RPL7A │ │ ├── S100A7A │ │ ├── SDR9C7 │ │ ├── SLC25A2 │ │ ├── SS18L2 │ │ ├── SUPT16H │ │ ├── TIMM29 │ │ ├── TMPRSS11F │ │ └── ZP2 │ └── src │ │ ├── gene_essentiality_cancer_createRandomGeneSubset.R │ │ ├── main_fxns.R │ │ └── summarize_gene_essentiality_cancer.R ├── glioblastoma_survival │ ├── ai_messages │ │ ├── baseline--generated_EGFR_MGMT_low_methylated │ │ ├── baseline--generated_NF1_MGMT_medium_methylated │ │ ├── baseline--generated_PIK3CA_MGMT_high_methylated │ │ ├── baseline--generated_PTEN_EGFR_MGMT_high_methylated │ │ ├── baseline--generated_PTEN_MGMT_medium_methylated │ │ ├── baseline--generated_PTEN_MUC16_MGMT_medium_methylated │ │ ├── baseline--generated_PTEN_NF1_MGMT_low_methylated │ │ ├── baseline--generated_PTEN_TP53_NF1_RB1_MGMT_medium_methylated │ │ ├── baseline--generated_TP53_MGMT_medium_methylated │ │ ├── baseline--generated_TP53_PIK3CA_MGMT_medium_methylated │ │ ├── high_complexity--generated_EGFR_MGMT_low_methylated │ │ ├── high_complexity--generated_NF1_MGMT_medium_methylated │ │ ├── high_complexity--generated_PIK3CA_MGMT_high_methylated │ │ ├── high_complexity--generated_PTEN_EGFR_MGMT_high_methylated │ │ ├── high_complexity--generated_PTEN_MGMT_medium_methylated │ │ ├── high_complexity--generated_PTEN_MUC16_MGMT_medium_methylated │ │ ├── high_complexity--generated_PTEN_NF1_MGMT_low_methylated │ │ ├── high_complexity--generated_PTEN_TP53_NF1_RB1_MGMT_medium_methylated │ │ ├── high_complexity--generated_TP53_MGMT_medium_methylated │ │ └── high_complexity--generated_TP53_PIK3CA_MGMT_medium_methylated │ ├── analysis.py │ ├── data │ │ ├── cases.csv │ │ ├── clinical.tsv │ │ ├── gbm_cptac_2021 │ │ │ └── data_methylation_epic_MGMT.txt │ │ ├── means.csv │ │ └── results.csv │ ├── main.md │ ├── main.py │ ├── prompt_template │ └── prompts │ │ ├── generated_EGFR_MGMT_low_methylated │ │ ├── generated_NF1_MGMT_medium_methylated │ │ ├── generated_PIK3CA_MGMT_high_methylated │ │ ├── generated_PTEN_EGFR_MGMT_high_methylated │ │ ├── generated_PTEN_MGMT_medium_methylated │ │ ├── generated_PTEN_MUC16_MGMT_medium_methylated │ │ ├── generated_PTEN_NF1_MGMT_low_methylated │ │ ├── generated_PTEN_TP53_NF1_RB1_MGMT_medium_methylated │ │ ├── generated_TP53_MGMT_medium_methylated │ │ └── generated_TP53_PIK3CA_MGMT_medium_methylated ├── mouse_immunology │ ├── ai_messages │ │ ├── baseline--Cyanide │ │ ├── baseline--YUMM_1_7 │ │ ├── high_complexity--Cyanide │ │ └── high_complexity--YUMM_1_7 │ └── prompts │ │ ├── Cyanide │ │ └── YUMM_1_7 ├── sepsis_treatment │ ├── ai_messages │ │ ├── baseline--sepsis_hyperinflammation │ │ ├── baseline--sepsis_immunoparalysis │ │ ├── high_complexity--sepsis_hyperinflammation │ │ ├── high_complexity--sepsis_immunoparalysis │ │ ├── low_complexity--sepsis_hyperinflammation │ │ └── low_complexity--sepsis_immunoparalysis │ └── prompts │ │ ├── sepsis_hyperinflammation │ │ └── sepsis_immunoparalysis └── trained_immunity │ ├── ai_messages │ ├── baseline--tolerance_LPS │ ├── baseline--training_LPS │ ├── baseline--training_betaglucan │ ├── high_complexity--tolerance_LPS │ ├── high_complexity--training_LPS │ └── high_complexity--training_betaglucan │ └── prompts │ ├── tolerance_LPS │ ├── training_LPS │ └── training_betaglucan ├── expert_feedback ├── README.md ├── annotation.csv ├── expert_feedback_extraction.py ├── expert_feedback_form_generator.py ├── expert_feedback_statistics.R ├── expert_feedback_visualization.R ├── feedback_form.xlsx ├── feedback_form_FINAL_experts.xlsx ├── feedback_form_FINAL_groundtruth.xlsx ├── likert_scale.png ├── questions.txt └── results │ ├── expert_feedback_results.csv │ ├── feedback_form_expert1.xlsx │ ├── feedback_form_expert2.xlsx │ ├── feedback_form_expert3.xlsx │ └── stats │ ├── AGGREGATED.csv │ ├── EXP_ALL.csv │ ├── EXP_glioblastoma_survival.csv │ ├── EXP_mouse_immunology.csv │ ├── EXP_sepsis_treatment.csv │ ├── EXP_trained_immunity.csv │ ├── OUT_ALL.csv │ ├── OUT_glioblastoma_survival.csv │ ├── OUT_mouse_immunology.csv │ ├── OUT_sepsis_treatment.csv │ ├── OUT_trained_immunity.csv │ └── SIM_sepsis_treatment.csv ├── game_of_life ├── ai_message ├── game_of_life_simulator.py ├── human_prompt ├── latex_output ├── main.md ├── python_output └── system_prompt ├── reference_analysis ├── all_manual_check.xlsx ├── crc_all.csv ├── cross_ref │ ├── crc--baseline--crc_apc_impact_2020_0.csv │ ├── crc--baseline--crc_apc_impact_2020_1.csv │ ├── crc--baseline--crc_apc_impact_2020_10.csv │ ├── crc--baseline--crc_apc_impact_2020_11.csv │ ├── crc--baseline--crc_apc_impact_2020_12.csv │ ├── crc--baseline--crc_apc_impact_2020_13.csv │ ├── crc--baseline--crc_apc_impact_2020_14.csv │ ├── crc--baseline--crc_apc_impact_2020_15.csv │ ├── crc--baseline--crc_apc_impact_2020_16.csv │ ├── crc--baseline--crc_apc_impact_2020_17.csv │ ├── crc--baseline--crc_apc_impact_2020_18.csv │ ├── crc--baseline--crc_apc_impact_2020_19.csv │ ├── crc--baseline--crc_apc_impact_2020_20.csv │ ├── crc--baseline--crc_apc_impact_2020_3.csv │ ├── crc--baseline--crc_apc_impact_2020_5.csv │ ├── crc--baseline--crc_apc_impact_2020_6.csv │ ├── crc--baseline--crc_apc_impact_2020_8.csv │ ├── crc--baseline--crc_apc_impact_2020_9.csv │ ├── crc--high_complexity--crc_apc_impact_2020_0.csv │ ├── crc--high_complexity--crc_apc_impact_2020_1.csv │ ├── crc--high_complexity--crc_apc_impact_2020_10.csv │ ├── crc--high_complexity--crc_apc_impact_2020_11.csv │ ├── crc--high_complexity--crc_apc_impact_2020_12.csv │ ├── crc--high_complexity--crc_apc_impact_2020_13.csv │ ├── crc--high_complexity--crc_apc_impact_2020_14.csv │ ├── crc--high_complexity--crc_apc_impact_2020_15.csv │ ├── crc--high_complexity--crc_apc_impact_2020_16.csv │ ├── crc--high_complexity--crc_apc_impact_2020_17.csv │ ├── crc--high_complexity--crc_apc_impact_2020_18.csv │ ├── crc--high_complexity--crc_apc_impact_2020_19.csv │ ├── crc--high_complexity--crc_apc_impact_2020_20.csv │ ├── crc--high_complexity--crc_apc_impact_2020_3.csv │ ├── crc--high_complexity--crc_apc_impact_2020_5.csv │ ├── crc--high_complexity--crc_apc_impact_2020_6.csv │ ├── crc--high_complexity--crc_apc_impact_2020_8.csv │ ├── crc--high_complexity--crc_apc_impact_2020_9.csv │ ├── gene_essentiality_cancer--baseline--AGAP5.csv │ ├── gene_essentiality_cancer--baseline--ANKLE2.csv │ ├── gene_essentiality_cancer--baseline--AQP8.csv │ ├── gene_essentiality_cancer--baseline--CCT5.csv │ ├── gene_essentiality_cancer--baseline--DCTN3.csv │ ├── gene_essentiality_cancer--baseline--DHX16.csv │ ├── gene_essentiality_cancer--baseline--DSPP.csv │ ├── gene_essentiality_cancer--baseline--EIF2B3.csv │ ├── gene_essentiality_cancer--baseline--FAM71C.csv │ ├── gene_essentiality_cancer--baseline--GK2.csv │ ├── gene_essentiality_cancer--baseline--GPR78.csv │ ├── gene_essentiality_cancer--baseline--GTF3C1.csv │ ├── gene_essentiality_cancer--baseline--H2BC1.csv │ ├── gene_essentiality_cancer--baseline--HAUS4.csv │ ├── gene_essentiality_cancer--baseline--HNRNPK.csv │ ├── gene_essentiality_cancer--baseline--HNRNPL.csv │ ├── gene_essentiality_cancer--baseline--HSPD1.csv │ ├── gene_essentiality_cancer--baseline--IFNA10.csv │ ├── gene_essentiality_cancer--baseline--IL12B.csv │ ├── gene_essentiality_cancer--baseline--IL17F.csv │ ├── gene_essentiality_cancer--baseline--KIF2B.csv │ ├── gene_essentiality_cancer--baseline--KRT25.csv │ ├── gene_essentiality_cancer--baseline--KRT26.csv │ ├── gene_essentiality_cancer--baseline--KRT82.csv │ ├── gene_essentiality_cancer--baseline--KRTAP9-3.csv │ ├── gene_essentiality_cancer--baseline--LGALS14.csv │ ├── gene_essentiality_cancer--baseline--MRGPRX2.csv │ ├── gene_essentiality_cancer--baseline--NDST4.csv │ ├── gene_essentiality_cancer--baseline--NDUFB3.csv │ ├── gene_essentiality_cancer--baseline--NOC3L.csv │ ├── gene_essentiality_cancer--baseline--OPA1.csv │ ├── gene_essentiality_cancer--baseline--OR3A3.csv │ ├── gene_essentiality_cancer--baseline--OR52W1.csv │ ├── gene_essentiality_cancer--baseline--OR5T2.csv │ ├── gene_essentiality_cancer--baseline--POLR3H.csv │ ├── gene_essentiality_cancer--baseline--PRPF19.csv │ ├── gene_essentiality_cancer--baseline--PSMA7.csv │ ├── gene_essentiality_cancer--baseline--PSMB7.csv │ ├── gene_essentiality_cancer--baseline--RBX1.csv │ ├── gene_essentiality_cancer--baseline--RPL27.csv │ ├── gene_essentiality_cancer--baseline--RPL37A.csv │ ├── gene_essentiality_cancer--baseline--RPL7A.csv │ ├── gene_essentiality_cancer--baseline--S100A7A.csv │ ├── gene_essentiality_cancer--baseline--SDR9C7.csv │ ├── gene_essentiality_cancer--baseline--SLC25A2.csv │ ├── gene_essentiality_cancer--baseline--SS18L2.csv │ ├── gene_essentiality_cancer--baseline--SUPT16H.csv │ ├── gene_essentiality_cancer--baseline--TIMM29.csv │ ├── gene_essentiality_cancer--baseline--TMPRSS11F.csv │ ├── gene_essentiality_cancer--baseline--ZP2.csv │ ├── gene_essentiality_cancer--high_complexity--AGAP5.csv │ ├── gene_essentiality_cancer--high_complexity--ANKLE2.csv │ ├── gene_essentiality_cancer--high_complexity--AQP8.csv │ ├── gene_essentiality_cancer--high_complexity--CCT5.csv │ ├── gene_essentiality_cancer--high_complexity--DCTN3.csv │ ├── gene_essentiality_cancer--high_complexity--DHX16.csv │ ├── gene_essentiality_cancer--high_complexity--DSPP.csv │ ├── gene_essentiality_cancer--high_complexity--EIF2B3.csv │ ├── gene_essentiality_cancer--high_complexity--FAM71C.csv │ ├── gene_essentiality_cancer--high_complexity--GK2.csv │ ├── gene_essentiality_cancer--high_complexity--GPR78.csv │ ├── gene_essentiality_cancer--high_complexity--GTF3C1.csv │ ├── gene_essentiality_cancer--high_complexity--H2BC1.csv │ ├── gene_essentiality_cancer--high_complexity--HAUS4.csv │ ├── gene_essentiality_cancer--high_complexity--HNRNPK.csv │ ├── gene_essentiality_cancer--high_complexity--HNRNPL.csv │ ├── gene_essentiality_cancer--high_complexity--HSPD1.csv │ ├── gene_essentiality_cancer--high_complexity--IFNA10.csv │ ├── gene_essentiality_cancer--high_complexity--IL12B.csv │ ├── gene_essentiality_cancer--high_complexity--IL17F.csv │ ├── gene_essentiality_cancer--high_complexity--KIF2B.csv │ ├── gene_essentiality_cancer--high_complexity--KRT25.csv │ ├── gene_essentiality_cancer--high_complexity--KRT26.csv │ ├── gene_essentiality_cancer--high_complexity--KRT82.csv │ ├── gene_essentiality_cancer--high_complexity--KRTAP9-3.csv │ ├── gene_essentiality_cancer--high_complexity--LGALS14.csv │ ├── gene_essentiality_cancer--high_complexity--MRGPRX2.csv │ ├── gene_essentiality_cancer--high_complexity--NDST4.csv │ ├── gene_essentiality_cancer--high_complexity--NDUFB3.csv │ ├── gene_essentiality_cancer--high_complexity--NOC3L.csv │ ├── gene_essentiality_cancer--high_complexity--OPA1.csv │ ├── gene_essentiality_cancer--high_complexity--OR3A3.csv │ ├── gene_essentiality_cancer--high_complexity--OR52W1.csv │ ├── gene_essentiality_cancer--high_complexity--OR5T2.csv │ ├── gene_essentiality_cancer--high_complexity--POLR3H.csv │ ├── gene_essentiality_cancer--high_complexity--PRPF19.csv │ ├── gene_essentiality_cancer--high_complexity--PSMA7.csv │ ├── gene_essentiality_cancer--high_complexity--PSMB7.csv │ ├── gene_essentiality_cancer--high_complexity--RBX1.csv │ ├── gene_essentiality_cancer--high_complexity--RPL27.csv │ ├── gene_essentiality_cancer--high_complexity--RPL37A.csv │ ├── gene_essentiality_cancer--high_complexity--RPL7A.csv │ ├── gene_essentiality_cancer--high_complexity--S100A7A.csv │ ├── gene_essentiality_cancer--high_complexity--SDR9C7.csv │ ├── gene_essentiality_cancer--high_complexity--SLC25A2.csv │ ├── gene_essentiality_cancer--high_complexity--SS18L2.csv │ ├── gene_essentiality_cancer--high_complexity--SUPT16H.csv │ ├── gene_essentiality_cancer--high_complexity--TIMM29.csv │ ├── gene_essentiality_cancer--high_complexity--TMPRSS11F.csv │ ├── gene_essentiality_cancer--high_complexity--ZP2.csv │ ├── glioblastoma_survival--baseline--generated_EGFR_MGMT_low_methylated.csv │ ├── glioblastoma_survival--baseline--generated_NF1_MGMT_medium_methylated.csv │ ├── glioblastoma_survival--baseline--generated_PIK3CA_MGMT_high_methylated.csv │ ├── glioblastoma_survival--baseline--generated_PTEN_EGFR_MGMT_high_methylated.csv │ ├── glioblastoma_survival--baseline--generated_PTEN_MGMT_medium_methylated.csv │ ├── glioblastoma_survival--baseline--generated_PTEN_MUC16_MGMT_medium_methylated.csv │ ├── glioblastoma_survival--baseline--generated_PTEN_NF1_MGMT_low_methylated.csv │ ├── glioblastoma_survival--baseline--generated_PTEN_TP53_NF1_RB1_MGMT_medium_methylated.csv │ ├── glioblastoma_survival--baseline--generated_TP53_MGMT_medium_methylated.csv │ ├── glioblastoma_survival--baseline--generated_TP53_PIK3CA_MGMT_medium_methylated.csv │ ├── glioblastoma_survival--high_complexity--generated_EGFR_MGMT_low_methylated.csv │ ├── glioblastoma_survival--high_complexity--generated_NF1_MGMT_medium_methylated.csv │ ├── glioblastoma_survival--high_complexity--generated_PIK3CA_MGMT_high_methylated.csv │ ├── glioblastoma_survival--high_complexity--generated_PTEN_EGFR_MGMT_high_methylated.csv │ ├── glioblastoma_survival--high_complexity--generated_PTEN_MGMT_medium_methylated.csv │ ├── glioblastoma_survival--high_complexity--generated_PTEN_MUC16_MGMT_medium_methylated.csv │ ├── glioblastoma_survival--high_complexity--generated_PTEN_NF1_MGMT_low_methylated.csv │ ├── glioblastoma_survival--high_complexity--generated_PTEN_TP53_NF1_RB1_MGMT_medium_methylated.csv │ ├── glioblastoma_survival--high_complexity--generated_TP53_MGMT_medium_methylated.csv │ ├── glioblastoma_survival--high_complexity--generated_TP53_PIK3CA_MGMT_medium_methylated.csv │ ├── mouse_immunology--baseline--Cyanide.csv │ ├── mouse_immunology--baseline--YUMM_1_7.csv │ ├── mouse_immunology--high_complexity--Cyanide.csv │ ├── mouse_immunology--high_complexity--YUMM_1_7.csv │ ├── sepsis_treatment--baseline--sepsis_hyperinflammation.csv │ ├── sepsis_treatment--baseline--sepsis_immunoparalysis.csv │ ├── sepsis_treatment--high_complexity--sepsis_hyperinflammation.csv │ ├── sepsis_treatment--high_complexity--sepsis_immunoparalysis.csv │ ├── trained_immunity--baseline--tolerance_LPS.csv │ ├── trained_immunity--baseline--training_LPS.csv │ ├── trained_immunity--baseline--training_betaglucan.csv │ ├── trained_immunity--high_complexity--tolerance_LPS.csv │ ├── trained_immunity--high_complexity--training_LPS.csv │ └── trained_immunity--high_complexity--training_betaglucan.csv ├── gene_essentiality_cancer_all.csv ├── glioblastoma_survival_all.csv ├── matched │ ├── crc--baseline--crc_apc_impact_2020_0.csv │ ├── crc--baseline--crc_apc_impact_2020_1.csv │ ├── crc--baseline--crc_apc_impact_2020_10.csv │ ├── crc--baseline--crc_apc_impact_2020_11.csv │ ├── crc--baseline--crc_apc_impact_2020_12.csv │ ├── crc--baseline--crc_apc_impact_2020_13.csv │ ├── crc--baseline--crc_apc_impact_2020_14.csv │ ├── crc--baseline--crc_apc_impact_2020_15.csv │ ├── crc--baseline--crc_apc_impact_2020_16.csv │ ├── crc--baseline--crc_apc_impact_2020_17.csv │ ├── crc--baseline--crc_apc_impact_2020_18.csv │ ├── crc--baseline--crc_apc_impact_2020_19.csv │ ├── crc--baseline--crc_apc_impact_2020_20.csv │ ├── crc--baseline--crc_apc_impact_2020_3.csv │ ├── crc--baseline--crc_apc_impact_2020_5.csv │ ├── crc--baseline--crc_apc_impact_2020_6.csv │ ├── crc--baseline--crc_apc_impact_2020_8.csv │ ├── crc--baseline--crc_apc_impact_2020_9.csv │ ├── crc--high_complexity--crc_apc_impact_2020_0.csv │ ├── crc--high_complexity--crc_apc_impact_2020_1.csv │ ├── crc--high_complexity--crc_apc_impact_2020_10.csv │ ├── crc--high_complexity--crc_apc_impact_2020_11.csv │ ├── crc--high_complexity--crc_apc_impact_2020_12.csv │ ├── crc--high_complexity--crc_apc_impact_2020_13.csv │ ├── crc--high_complexity--crc_apc_impact_2020_14.csv │ ├── crc--high_complexity--crc_apc_impact_2020_15.csv │ ├── crc--high_complexity--crc_apc_impact_2020_16.csv │ ├── crc--high_complexity--crc_apc_impact_2020_17.csv │ ├── crc--high_complexity--crc_apc_impact_2020_18.csv │ ├── crc--high_complexity--crc_apc_impact_2020_19.csv │ ├── crc--high_complexity--crc_apc_impact_2020_20.csv │ ├── crc--high_complexity--crc_apc_impact_2020_3.csv │ ├── crc--high_complexity--crc_apc_impact_2020_5.csv │ ├── crc--high_complexity--crc_apc_impact_2020_6.csv │ ├── crc--high_complexity--crc_apc_impact_2020_8.csv │ ├── crc--high_complexity--crc_apc_impact_2020_9.csv │ ├── gene_essentiality_cancer--baseline--AGAP5.csv │ ├── gene_essentiality_cancer--baseline--ANKLE2.csv │ ├── gene_essentiality_cancer--baseline--AQP8.csv │ ├── gene_essentiality_cancer--baseline--CCT5.csv │ ├── gene_essentiality_cancer--baseline--DCTN3.csv │ ├── gene_essentiality_cancer--baseline--DHX16.csv │ ├── gene_essentiality_cancer--baseline--DSPP.csv │ ├── gene_essentiality_cancer--baseline--EIF2B3.csv │ ├── gene_essentiality_cancer--baseline--FAM71C.csv │ ├── gene_essentiality_cancer--baseline--GK2.csv │ ├── gene_essentiality_cancer--baseline--GPR78.csv │ ├── gene_essentiality_cancer--baseline--GTF3C1.csv │ ├── gene_essentiality_cancer--baseline--H2BC1.csv │ ├── gene_essentiality_cancer--baseline--HAUS4.csv │ ├── gene_essentiality_cancer--baseline--HNRNPK.csv │ ├── gene_essentiality_cancer--baseline--HNRNPL.csv │ ├── gene_essentiality_cancer--baseline--HSPD1.csv │ ├── gene_essentiality_cancer--baseline--IFNA10.csv │ ├── gene_essentiality_cancer--baseline--IL12B.csv │ ├── gene_essentiality_cancer--baseline--IL17F.csv │ ├── gene_essentiality_cancer--baseline--KIF2B.csv │ ├── gene_essentiality_cancer--baseline--KRT25.csv │ ├── gene_essentiality_cancer--baseline--KRT26.csv │ ├── gene_essentiality_cancer--baseline--KRT82.csv │ ├── gene_essentiality_cancer--baseline--KRTAP9-3.csv │ ├── gene_essentiality_cancer--baseline--LGALS14.csv │ ├── gene_essentiality_cancer--baseline--MRGPRX2.csv │ ├── gene_essentiality_cancer--baseline--NDST4.csv │ ├── gene_essentiality_cancer--baseline--NDUFB3.csv │ ├── gene_essentiality_cancer--baseline--NOC3L.csv │ ├── gene_essentiality_cancer--baseline--OPA1.csv │ ├── gene_essentiality_cancer--baseline--OR3A3.csv │ ├── gene_essentiality_cancer--baseline--OR52W1.csv │ ├── gene_essentiality_cancer--baseline--OR5T2.csv │ ├── gene_essentiality_cancer--baseline--POLR3H.csv │ ├── gene_essentiality_cancer--baseline--PRPF19.csv │ ├── gene_essentiality_cancer--baseline--PSMA7.csv │ ├── gene_essentiality_cancer--baseline--PSMB7.csv │ ├── gene_essentiality_cancer--baseline--RBX1.csv │ ├── gene_essentiality_cancer--baseline--RPL27.csv │ ├── gene_essentiality_cancer--baseline--RPL37A.csv │ ├── gene_essentiality_cancer--baseline--RPL7A.csv │ ├── gene_essentiality_cancer--baseline--S100A7A.csv │ ├── gene_essentiality_cancer--baseline--SDR9C7.csv │ ├── gene_essentiality_cancer--baseline--SLC25A2.csv │ ├── gene_essentiality_cancer--baseline--SS18L2.csv │ ├── gene_essentiality_cancer--baseline--SUPT16H.csv │ ├── gene_essentiality_cancer--baseline--TIMM29.csv │ ├── gene_essentiality_cancer--baseline--TMPRSS11F.csv │ ├── gene_essentiality_cancer--baseline--ZP2.csv │ ├── gene_essentiality_cancer--high_complexity--AGAP5.csv │ ├── gene_essentiality_cancer--high_complexity--ANKLE2.csv │ ├── gene_essentiality_cancer--high_complexity--AQP8.csv │ ├── gene_essentiality_cancer--high_complexity--CCT5.csv │ ├── gene_essentiality_cancer--high_complexity--DCTN3.csv │ ├── gene_essentiality_cancer--high_complexity--DHX16.csv │ ├── gene_essentiality_cancer--high_complexity--DSPP.csv │ ├── gene_essentiality_cancer--high_complexity--EIF2B3.csv │ ├── gene_essentiality_cancer--high_complexity--FAM71C.csv │ ├── gene_essentiality_cancer--high_complexity--GK2.csv │ ├── gene_essentiality_cancer--high_complexity--GPR78.csv │ ├── gene_essentiality_cancer--high_complexity--GTF3C1.csv │ ├── gene_essentiality_cancer--high_complexity--H2BC1.csv │ ├── gene_essentiality_cancer--high_complexity--HAUS4.csv │ ├── gene_essentiality_cancer--high_complexity--HNRNPK.csv │ ├── gene_essentiality_cancer--high_complexity--HNRNPL.csv │ ├── gene_essentiality_cancer--high_complexity--HSPD1.csv │ ├── gene_essentiality_cancer--high_complexity--IFNA10.csv │ ├── gene_essentiality_cancer--high_complexity--IL12B.csv │ ├── gene_essentiality_cancer--high_complexity--IL17F.csv │ ├── gene_essentiality_cancer--high_complexity--KIF2B.csv │ ├── gene_essentiality_cancer--high_complexity--KRT25.csv │ ├── gene_essentiality_cancer--high_complexity--KRT26.csv │ ├── gene_essentiality_cancer--high_complexity--KRT82.csv │ ├── gene_essentiality_cancer--high_complexity--KRTAP9-3.csv │ ├── gene_essentiality_cancer--high_complexity--LGALS14.csv │ ├── gene_essentiality_cancer--high_complexity--MRGPRX2.csv │ ├── gene_essentiality_cancer--high_complexity--NDST4.csv │ ├── gene_essentiality_cancer--high_complexity--NDUFB3.csv │ ├── gene_essentiality_cancer--high_complexity--NOC3L.csv │ ├── gene_essentiality_cancer--high_complexity--OPA1.csv │ ├── gene_essentiality_cancer--high_complexity--OR3A3.csv │ ├── gene_essentiality_cancer--high_complexity--OR52W1.csv │ ├── gene_essentiality_cancer--high_complexity--OR5T2.csv │ ├── gene_essentiality_cancer--high_complexity--POLR3H.csv │ ├── gene_essentiality_cancer--high_complexity--PRPF19.csv │ ├── gene_essentiality_cancer--high_complexity--PSMA7.csv │ ├── gene_essentiality_cancer--high_complexity--PSMB7.csv │ ├── gene_essentiality_cancer--high_complexity--RBX1.csv │ ├── gene_essentiality_cancer--high_complexity--RPL27.csv │ ├── gene_essentiality_cancer--high_complexity--RPL37A.csv │ ├── gene_essentiality_cancer--high_complexity--RPL7A.csv │ ├── gene_essentiality_cancer--high_complexity--S100A7A.csv │ ├── gene_essentiality_cancer--high_complexity--SDR9C7.csv │ ├── gene_essentiality_cancer--high_complexity--SLC25A2.csv │ ├── gene_essentiality_cancer--high_complexity--SS18L2.csv │ ├── gene_essentiality_cancer--high_complexity--SUPT16H.csv │ ├── gene_essentiality_cancer--high_complexity--TIMM29.csv │ ├── gene_essentiality_cancer--high_complexity--TMPRSS11F.csv │ ├── gene_essentiality_cancer--high_complexity--ZP2.csv │ ├── glioblastoma_survival--baseline--generated_EGFR_MGMT_low_methylated.csv │ ├── glioblastoma_survival--baseline--generated_NF1_MGMT_medium_methylated.csv │ ├── glioblastoma_survival--baseline--generated_PIK3CA_MGMT_high_methylated.csv │ ├── glioblastoma_survival--baseline--generated_PTEN_EGFR_MGMT_high_methylated.csv │ ├── glioblastoma_survival--baseline--generated_PTEN_MGMT_medium_methylated.csv │ ├── glioblastoma_survival--baseline--generated_PTEN_MUC16_MGMT_medium_methylated.csv │ ├── glioblastoma_survival--baseline--generated_PTEN_NF1_MGMT_low_methylated.csv │ ├── glioblastoma_survival--baseline--generated_PTEN_TP53_NF1_RB1_MGMT_medium_methylated.csv │ ├── glioblastoma_survival--baseline--generated_TP53_MGMT_medium_methylated.csv │ ├── glioblastoma_survival--baseline--generated_TP53_PIK3CA_MGMT_medium_methylated.csv │ ├── glioblastoma_survival--high_complexity--generated_EGFR_MGMT_low_methylated.csv │ ├── glioblastoma_survival--high_complexity--generated_NF1_MGMT_medium_methylated.csv │ ├── glioblastoma_survival--high_complexity--generated_PIK3CA_MGMT_high_methylated.csv │ ├── glioblastoma_survival--high_complexity--generated_PTEN_EGFR_MGMT_high_methylated.csv │ ├── glioblastoma_survival--high_complexity--generated_PTEN_MGMT_medium_methylated.csv │ ├── glioblastoma_survival--high_complexity--generated_PTEN_MUC16_MGMT_medium_methylated.csv │ ├── glioblastoma_survival--high_complexity--generated_PTEN_NF1_MGMT_low_methylated.csv │ ├── glioblastoma_survival--high_complexity--generated_PTEN_TP53_NF1_RB1_MGMT_medium_methylated.csv │ ├── glioblastoma_survival--high_complexity--generated_TP53_MGMT_medium_methylated.csv │ ├── glioblastoma_survival--high_complexity--generated_TP53_PIK3CA_MGMT_medium_methylated.csv │ ├── mouse_immunology--baseline--Cyanide.csv │ ├── mouse_immunology--baseline--YUMM_1_7.csv │ ├── mouse_immunology--high_complexity--Cyanide.csv │ ├── mouse_immunology--high_complexity--YUMM_1_7.csv │ ├── sepsis_treatment--baseline--sepsis_hyperinflammation.csv │ ├── sepsis_treatment--baseline--sepsis_immunoparalysis.csv │ ├── sepsis_treatment--high_complexity--sepsis_hyperinflammation.csv │ ├── sepsis_treatment--high_complexity--sepsis_immunoparalysis.csv │ ├── trained_immunity--baseline--tolerance_LPS.csv │ ├── trained_immunity--baseline--training_LPS.csv │ ├── trained_immunity--baseline--training_betaglucan.csv │ ├── trained_immunity--high_complexity--tolerance_LPS.csv │ ├── trained_immunity--high_complexity--training_LPS.csv │ └── trained_immunity--high_complexity--training_betaglucan.csv ├── mouse_immunology_all.csv ├── sepsis_treatment_all.csv └── trained_immunity_all.csv ├── simulateGPT_logo.png ├── src ├── check_references.py ├── match_references.py ├── rule_simulate.py └── utils.py └── system_messages ├── baseline ├── high5step_complexity ├── high_complexity └── low_complexity /.gitignore: -------------------------------------------------------------------------------- 1 | *.pyc 2 | .snakemake 3 | .snakemake_* 4 | .ipynb_checkpoints 5 | logs/ 6 | *.tar.gz -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2023 OpenBioLink 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /SimulateGPT_graphical_abstract.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/OpenBioLink/SimulateGPT/dd1ba1a876888e6c03633b4b6aae9634df02932c/SimulateGPT_graphical_abstract.jpg -------------------------------------------------------------------------------- /config.yaml: -------------------------------------------------------------------------------- 1 | system_names: 2 | - "baseline" 3 | # - "low_complexity" 4 | - "high_complexity" 5 | # - "high5step_complexity" 6 | experiment_name: "glioblastoma_survival" # default, override 7 | -------------------------------------------------------------------------------- /experiments/crc/.gitignore: -------------------------------------------------------------------------------- 1 | *.tar.gz 2 | crc_apc_impact_2020/ -------------------------------------------------------------------------------- /experiments/crc/ai_messages/baseline--crc_apc_impact_2020_0: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: 18 months 3 | explanation: In the given case, the patient is diagnosed with stage II colon adenocarcinoma, which is an earlier stage compared to the examples provided. The tumor exhibits moderately differentiated differentiation histology, which is generally associated with a better prognosis than poorly differentiated histology. Additionally, there are no signs of carcinomatosis, which is a positive factor for the patient's prognosis. Under an optimal standard of care treatment plan, the patient is expected to have a longer progression-free survival than the examples provided. Considering the age and stage of the disease, an estimated progression-free survival of 18 months is reasonable. However, it is important to note that individual responses to treatment may vary, and this estimate is based on the limited information provided. -------------------------------------------------------------------------------- /experiments/crc/ai_messages/baseline--crc_apc_impact_2020_1: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: 18 months 3 | explanation: In the given case, the patient has stage II rectal adenocarcinoma with moderately differentiated differentiation histology, metastasis, and no signs of carcinomatosis. Comparing this case to the provided examples, the patient's cancer stage is lower than those in the examples, and the differentiation histology is moderately differentiated, which is generally associated with a better prognosis than poorly differentiated tumors. Under an optimal standard of care treatment plan, which may include surgery, chemotherapy, and radiation therapy, the patient is expected to have a longer progression-free survival than the patients in the examples. Considering these factors, an estimated progression-free survival of 18 months is inferred. -------------------------------------------------------------------------------- /experiments/crc/ai_messages/baseline--crc_apc_impact_2020_10: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: 8 months of progression-free survival 3 | explanation: Based on the provided examples, we can infer that the patient's age, tumor stage, differentiation histology, and presence of carcinomatosis are factors that may influence progression-free survival. In this case, the patient is 56 years old with stage IV colon adenocarcinoma, moderately differentiated differentiation histology, metastasis, and no signs of carcinomatosis. Comparing this case to the examples, we can observe that the patient's age and tumor stage are similar to Example 1, while the differentiation histology and absence of carcinomatosis are more similar to Example 2 and 4. Considering the general effectiveness of colon adenocarcinoma therapies and the optimal standard of care treatment plan, we can estimate an expected progression-free survival of 8 months. This estimation takes into account the patient's moderately differentiated histology, which may respond better to treatment than poorly differentiated tumors, and the absence of carcinomatosis, which is associated with a better prognosis. -------------------------------------------------------------------------------- /experiments/crc/ai_messages/baseline--crc_apc_impact_2020_11: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: 6 months 3 | explanation: Based on the provided examples, we can infer that the patient's expected progression-free survival is around 6 months. This estimation takes into account the stage IV diagnosis, moderately differentiated differentiation histology, metastasis, and presence of carcinomatosis. The optimal standard of care treatment plan would likely include a combination of chemotherapy, targeted therapy, and immunotherapy, which can help slow down tumor progression by targeting molecular and cellular processes involved in cancer growth and spread. However, given the advanced stage and presence of carcinomatosis, the prognosis remains relatively poor compared to earlier-stage cases. -------------------------------------------------------------------------------- /experiments/crc/ai_messages/baseline--crc_apc_impact_2020_12: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: 6 months of progression-free survival 3 | explanation: Based on the provided examples, patients with stage IV cancer and poorly differentiated histology tend to have shorter progression-free survival times. In Example 1, the patient with stage IV rectal adenocarcinoma had 2 months of progression-free survival, while patients with stage III colon adenocarcinoma had longer progression-free survival times (7-14 months). The patient in question has stage IV colon adenocarcinoma with poorly differentiated histology, which is more aggressive and harder to treat. Under an optimal standard of care treatment plan, the molecular and higher-level processes impacting tumor progression may be slowed down, but the overall prognosis remains poor. Considering the age and tumor characteristics, a 6-month progression-free survival is estimated, which is slightly better than Example 1 due to the younger age and the focus on an optimal standard of care treatment plan. -------------------------------------------------------------------------------- /experiments/crc/ai_messages/baseline--crc_apc_impact_2020_13: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: 6 months 3 | explanation: Based on the provided examples, patients with stage IV cancer and poorly differentiated histology tend to have shorter progression-free survival times. In Example 1, the patient with stage IV rectal adenocarcinoma had a progression-free survival of 2 months. Although the current case involves colon adenocarcinoma, both are gastrointestinal cancers and share similarities in treatment and prognosis. The presence of carcinomatosis, as seen in Example 3, also contributes to a worse prognosis. Considering the patient's younger age and the general effectiveness of colon adenocarcinoma therapies, a slightly better outcome than Example 1 can be expected. Therefore, the estimated progression-free survival for this patient under an optimal standard of care treatment plan is 6 months. -------------------------------------------------------------------------------- /experiments/crc/ai_messages/baseline--crc_apc_impact_2020_14: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: 6 months of progression-free survival 3 | explanation: Based on the provided examples, the patient's age, stage IV diagnosis, and moderately differentiated differentiation histology are factors that can be used to estimate the progression-free survival. Example 1 has a stage IV diagnosis and poorly differentiated histology, resulting in 2 months of progression-free survival. Example 2 has a stage III diagnosis and poorly differentiated histology, resulting in 14 months of progression-free survival. Example 3 has a stage III diagnosis, moderately differentiated histology, and signs of carcinomatosis, resulting in 9 months of progression-free survival. Example 4 has a stage III diagnosis, moderately differentiated histology, and no signs of carcinomatosis, resulting in 7 months of progression-free survival. Considering the patient's stage IV diagnosis and moderately differentiated histology, the expected progression-free survival would be between Example 1 and Example 4. Given the general effectiveness of colon adenocarcinoma therapies and the absence of carcinomatosis, an estimate of 6 months of progression-free survival is reasonable. -------------------------------------------------------------------------------- /experiments/crc/ai_messages/baseline--crc_apc_impact_2020_15: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: 6 months of progression-free survival 3 | explanation: Based on the provided examples, we can infer that the patient's progression-free survival will be influenced by factors such as tumor stage, differentiation histology, metastasis, and presence of carcinomatosis. In this case, the patient has stage IV colon adenocarcinoma with moderately differentiated differentiation histology, metastasis, and no signs of carcinomatosis. Comparing this to the examples, the patient's condition is most similar to Example 1 (stage IV, poorly differentiated, metastasis, no carcinomatosis) and Example 3 (stage III, moderately differentiated, metastasis, carcinomatosis). Example 1 had 2 months of progression-free survival, while Example 3 had 9 months. Given that the patient's tumor is moderately differentiated (better prognosis than poorly differentiated) and has no signs of carcinomatosis (better prognosis than with carcinomatosis), we can estimate a slightly better outcome than the average of Example 1 and Example 3, resulting in an expected 6 months of progression-free survival under an optimal standard of care treatment plan. -------------------------------------------------------------------------------- /experiments/crc/ai_messages/baseline--crc_apc_impact_2020_16: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: 6 months of progression-free survival 3 | explanation: The patient has stage IV colon adenocarcinoma with moderately differentiated differentiation histology, metastasis, and signs of carcinomatosis. Comparing this case to the provided examples, the patient's condition is more severe than Example 2 and Example 4, which had stage III colon adenocarcinoma and longer progression-free survival times. However, the patient's tumor exhibits moderately differentiated histology, which is less aggressive than the poorly differentiated histology in Example 1, which had a shorter progression-free survival time. Considering these factors and the effectiveness of colon adenocarcinoma therapies, the patient is expected to have a progression-free survival of approximately 6 months under an optimal standard of care treatment plan. -------------------------------------------------------------------------------- /experiments/crc/ai_messages/baseline--crc_apc_impact_2020_17: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: 6 months of progression-free survival 3 | explanation: The patient has stage IV colon adenocarcinoma with poorly differentiated histology and metastasis, but no signs of carcinomatosis. Comparing this case to the provided examples, the patient's condition is more severe than Example 2 (stage III, poorly differentiated, 14 months PFS) but less severe than Example 1 (stage IV, poorly differentiated, rectal adenocarcinoma, 2 months PFS). Considering the general effectiveness of colon adenocarcinoma therapies and the patient's younger age, an optimal standard of care treatment plan may provide a slightly better outcome than the average of Examples 1 and 2. Therefore, the expected progression-free survival is estimated to be around 6 months. -------------------------------------------------------------------------------- /experiments/crc/ai_messages/baseline--crc_apc_impact_2020_18: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: 6 months 3 | explanation: Based on the provided examples, the patient's characteristics suggest a more aggressive disease course. The stage IV diagnosis, poorly differentiated histology, metastasis, and presence of carcinomatosis are all factors that contribute to a worse prognosis. Comparing to Example 1, which had a 2-month progression-free survival, this patient has colon adenocarcinoma instead of rectal adenocarcinoma, which generally has a better prognosis. However, the presence of carcinomatosis in this case, similar to Example 3, may reduce the progression-free survival compared to Example 2. Considering these factors, an estimated progression-free survival of 6 months is inferred under an optimal standard of care treatment plan. Molecular and higher-level processes impacting tumor progression include the effectiveness of chemotherapy, targeted therapies, and immunotherapies in controlling tumor growth, metastasis, and the tumor microenvironment. -------------------------------------------------------------------------------- /experiments/crc/ai_messages/baseline--crc_apc_impact_2020_19: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: 6 months of progression-free survival 3 | explanation: Based on the provided examples, patients with stage IV rectal adenocarcinoma (Example 1) tend to have shorter progression-free survival compared to those with stage III colon adenocarcinoma (Examples 2, 3, and 4). The patient in question has a moderately differentiated tumor, which is associated with better outcomes than poorly differentiated tumors (Examples 1 and 2). Additionally, the absence of carcinomatosis is a favorable factor (Examples 1, 2, and 4). Considering these factors and the effectiveness of rectal adenocarcinoma therapies, the patient is expected to have a progression-free survival of approximately 6 months under an optimal standard of care treatment plan. -------------------------------------------------------------------------------- /experiments/crc/ai_messages/baseline--crc_apc_impact_2020_20: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: 6 months of progression-free survival 3 | explanation: Based on the provided examples, we can infer that the patient's age, tumor stage, differentiation histology, metastasis, and presence of carcinomatosis are important factors in determining progression-free survival. In this case, the patient has stage IV rectal adenocarcinoma, which is more advanced than the stage III colon adenocarcinomas in Examples 2, 3, and 4. The tumor exhibits moderately differentiated differentiation histology, which is better than the poorly differentiated histology in Examples 1 and 2 but worse than the moderately differentiated histology in Examples 3 and 4. The tumor has metastasized and shows signs of carcinomatosis, similar to Example 3. Considering these factors and the general effectiveness of rectal adenocarcinoma therapies, we can estimate a progression-free survival of 6 months for this patient. This estimation takes into account the more advanced stage and presence of carcinomatosis compared to the other examples, as well as the moderately differentiated histology, which may provide a slightly better prognosis than poorly differentiated histology. -------------------------------------------------------------------------------- /experiments/crc/ai_messages/baseline--crc_apc_impact_2020_3: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: 6 months of progression-free survival 3 | explanation: The patient's age, stage III colon adenocarcinoma, moderately differentiated differentiation histology, metastasis, and presence of carcinomatosis are factors that influence the progression-free survival. Comparing this case to the provided examples, the patient's condition is most similar to Example 3, but with a moderately differentiated tumor instead of a poorly differentiated one. This difference may result in a slightly better prognosis. However, the patient's older age may negatively impact the effectiveness of the treatment. Considering these factors, the expected progression-free survival under an optimal standard of care treatment plan is estimated to be around 6 months. -------------------------------------------------------------------------------- /experiments/crc/ai_messages/baseline--crc_apc_impact_2020_5: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: 8 months of progression-free survival 3 | explanation: Based on the provided examples, patients with stage III colon adenocarcinoma and moderately differentiated differentiation histology tend to have progression-free survival between 7 and 9 months (Examples 3 and 4). The patient in question has a similar profile to Example 4, with the only difference being the tumor location (rectum vs. left side of the colon). Considering the general effectiveness of colon adenocarcinoma therapies and the absence of carcinomatosis, an optimal standard of care treatment plan would likely result in a progression-free survival of approximately 8 months for this patient. -------------------------------------------------------------------------------- /experiments/crc/ai_messages/baseline--crc_apc_impact_2020_6: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: 8 months of progression-free survival 3 | explanation: Based on the provided examples, patients with stage III colon adenocarcinoma and moderately differentiated differentiation histology tend to have a progression-free survival between 7 and 9 months. The patient in question has a similar profile to Example 3, but with no signs of carcinomatosis, which is a favorable factor. Considering the patient's age (71 years old) and the location of the tumor (right side of the colon), the expected progression-free survival under an optimal standard of care treatment plan is estimated to be around 8 months. This takes into account the general effectiveness of colon adenocarcinoma therapies and the molecular and higher-level processes impacting tumor progression. -------------------------------------------------------------------------------- /experiments/crc/ai_messages/baseline--crc_apc_impact_2020_8: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: 10 months 3 | explanation: Based on the provided examples, the patient's age, tumor stage, and differentiation histology are important factors in determining progression-free survival. In this case, the patient is 57 years old with stage III colon adenocarcinoma on the right side of the colon, poorly differentiated differentiation histology, metastasis, and no signs of carcinomatosis. Comparing this case to Example 2, which has a similar age and histology but is located on the left side of the colon, the patient had 14 months of progression-free survival. Example 3, which has a moderately differentiated histology and carcinomatosis, had 9 months of progression-free survival. Considering the similarities and differences between these examples, we can estimate a progression-free survival of around 10 months for this patient under an optimal standard of care treatment plan. The molecular and higher-level processes impacting tumor progression, such as the effectiveness of therapies targeting poorly differentiated cells and the absence of carcinomatosis, contribute to this estimated outcome. -------------------------------------------------------------------------------- /experiments/crc/ai_messages/baseline--crc_apc_impact_2020_9: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: 8 months 3 | explanation: Based on the provided examples, we can infer that the patient's progression-free survival would be around 8 months. This estimation takes into account the patient's age, stage III rectal adenocarcinoma diagnosis, moderately differentiated differentiation histology, metastasis, and absence of carcinomatosis. The optimal standard of care treatment plan would likely involve a combination of surgery, chemotherapy, and radiation therapy, which can help slow down tumor progression and improve progression-free survival. However, the patient's moderately differentiated histology and metastasis may still contribute to a relatively shorter progression-free survival compared to patients with better prognostic factors. -------------------------------------------------------------------------------- /experiments/crc/main.md: -------------------------------------------------------------------------------- 1 | # CRC survival 2 | 3 | see paper methods and source code for details 4 | 5 | - prompt_generation.py: Code to generate scenario prompts 6 | - analyze.py: Code to analyze LLM results 7 | 8 | 9 | Data was downloaded from https://cbioportal-datahub.s3.amazonaws.com/crc_apc_impact_2020.tar.gz and extracted in this folder 10 | -------------------------------------------------------------------------------- /experiments/crc/plots/CRC_baseline_high_raw.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/OpenBioLink/SimulateGPT/dd1ba1a876888e6c03633b4b6aae9634df02932c/experiments/crc/plots/CRC_baseline_high_raw.pdf -------------------------------------------------------------------------------- /experiments/crc/plots/CRC_high_complexity__baseline.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/OpenBioLink/SimulateGPT/dd1ba1a876888e6c03633b4b6aae9634df02932c/experiments/crc/plots/CRC_high_complexity__baseline.pdf -------------------------------------------------------------------------------- /experiments/crc/plots/CRC_high_complexity__baseline_edit1.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/OpenBioLink/SimulateGPT/dd1ba1a876888e6c03633b4b6aae9634df02932c/experiments/crc/plots/CRC_high_complexity__baseline_edit1.pdf -------------------------------------------------------------------------------- /experiments/crc/plots/CRC_high_complexity__high5step_complexity.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/OpenBioLink/SimulateGPT/dd1ba1a876888e6c03633b4b6aae9634df02932c/experiments/crc/plots/CRC_high_complexity__high5step_complexity.pdf -------------------------------------------------------------------------------- /experiments/crc/plots/CRC_high_complexity__high5step_complexity_edit1.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/OpenBioLink/SimulateGPT/dd1ba1a876888e6c03633b4b6aae9634df02932c/experiments/crc/plots/CRC_high_complexity__high5step_complexity_edit1.pdf -------------------------------------------------------------------------------- /experiments/crc/plots/CRC_high_complexity__low_complexity.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/OpenBioLink/SimulateGPT/dd1ba1a876888e6c03633b4b6aae9634df02932c/experiments/crc/plots/CRC_high_complexity__low_complexity.pdf -------------------------------------------------------------------------------- /experiments/crc/plots/CRC_high_complexity__low_complexity_edit1.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/OpenBioLink/SimulateGPT/dd1ba1a876888e6c03633b4b6aae9634df02932c/experiments/crc/plots/CRC_high_complexity__low_complexity_edit1.pdf -------------------------------------------------------------------------------- /experiments/crc/plots/all_in_one_reg.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/OpenBioLink/SimulateGPT/dd1ba1a876888e6c03633b4b6aae9634df02932c/experiments/crc/plots/all_in_one_reg.pdf -------------------------------------------------------------------------------- /experiments/crc/plots/all_in_one_reg.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/OpenBioLink/SimulateGPT/dd1ba1a876888e6c03633b4b6aae9634df02932c/experiments/crc/plots/all_in_one_reg.png -------------------------------------------------------------------------------- /experiments/crc/plots/baseline_vs_high_reg.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/OpenBioLink/SimulateGPT/dd1ba1a876888e6c03633b4b6aae9634df02932c/experiments/crc/plots/baseline_vs_high_reg.pdf -------------------------------------------------------------------------------- /experiments/crc/plots/baseline_vs_high_reg.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/OpenBioLink/SimulateGPT/dd1ba1a876888e6c03633b4b6aae9634df02932c/experiments/crc/plots/baseline_vs_high_reg.png -------------------------------------------------------------------------------- /experiments/crc/plots/pfs_regression_plot.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/OpenBioLink/SimulateGPT/dd1ba1a876888e6c03633b4b6aae9634df02932c/experiments/crc/plots/pfs_regression_plot.pdf -------------------------------------------------------------------------------- /experiments/crc/plots/pfs_regression_plot.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/OpenBioLink/SimulateGPT/dd1ba1a876888e6c03633b4b6aae9634df02932c/experiments/crc/plots/pfs_regression_plot.png -------------------------------------------------------------------------------- /experiments/crc/plots/subplots_reg.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/OpenBioLink/SimulateGPT/dd1ba1a876888e6c03633b4b6aae9634df02932c/experiments/crc/plots/subplots_reg.pdf -------------------------------------------------------------------------------- /experiments/crc/plots/subplots_reg.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/OpenBioLink/SimulateGPT/dd1ba1a876888e6c03633b4b6aae9634df02932c/experiments/crc/plots/subplots_reg.png -------------------------------------------------------------------------------- /experiments/crc/result_metrics.csv: -------------------------------------------------------------------------------- 1 | ,correlation,correlation_pval,spearman,spearman_pval,mae,rmsd,r2 2 | baseline,0.24504517964838782,0.3270599835151117,-0.029563097740653195,0.907299221596618,2.5805555555555557,14.444027777777778,0.060047140068910675 3 | low_complexity,0.4935219430040258,0.037399896353443,0.41364719302264635,0.08793846997024529,2.669444444444445,17.377361111111114,0.24356390822646917 4 | medium_complexity,0.4652549237806572,0.051700298189909066,0.34440045517141965,0.1616565418248944,2.0250000000000004,7.507916666666664,0.2164621441021451 5 | high_complexity,0.3135135315225405,0.20520228301116583,0.2650908305715425,0.2877314277716484,2.2972222222222225,7.677361111111111,0.09829073444773498 6 | high5step_complexity,0.3263292836762921,0.18629060771580241,0.38656361528478833,0.11304917624396708,2.0361111111111114,6.619027777777777,0.10649080138468196 7 | state_based,0.4601623877219694,0.05466339205489989,0.2493911205395664,0.3182782100353715,2.4083333333333337,9.244027777777777,0.2117494230739841 8 | -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/ai_messages/baseline--AGAP5: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: unlikely 3 | explanation: The AGAP5 gene is not well-known for its involvement in cancer cell lines. While it may have some role in cellular processes, it is not considered a key player in cancer development or progression. Therefore, it is unlikely that AGAP5 is essential in cancer cell-lines. -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/ai_messages/baseline--ANKLE2: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: unlikely 3 | explanation: ANKLE2 (Ankyrin Repeat And LEM Domain Containing 2) is not a well-known cancer-related gene, and there is limited evidence to suggest that it plays a significant role in cancer cell lines. While it may have some involvement in certain cellular processes, it is not considered essential for cancer cell survival or proliferation. -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/ai_messages/baseline--AQP8: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: unlikely 3 | explanation: AQP8 (Aquaporin 8) is a water channel protein that facilitates the transport of water across cell membranes. While it plays a role in various physiological processes, it is not typically considered essential for cancer cell survival or proliferation. Cancer cells often rely on other genes and pathways for their growth and survival, such as oncogenes and tumor suppressor genes. Therefore, it is unlikely that AQP8 is essential in cancer cell-lines. -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/ai_messages/baseline--CCT5: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: unlikely 3 | explanation: The CCT5 gene encodes a subunit of the chaperonin-containing T-complex (CCT), which is involved in protein folding. While it plays a role in cellular processes, it is not considered a key driver of cancer development or progression. Therefore, it is unlikely that the CCT5 gene is essential in cancer cell-lines. However, it is important to note that the essentiality of a gene can vary depending on the specific cancer type and context. -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/ai_messages/baseline--DCTN3: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: unlikely 3 | explanation: DCTN3 (Dynactin Subunit 3) is a component of the dynactin complex, which is involved in microtubule-based transport. While it plays a role in cellular processes, it is not commonly reported as an essential gene in cancer cell lines. Essential genes in cancer are typically those that are involved in cell proliferation, survival, and other critical pathways. However, the essentiality of a gene can vary depending on the specific cancer type and context. -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/ai_messages/baseline--DHX16: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: unlikely 3 | explanation: The gene DHX16 encodes for an RNA helicase involved in RNA metabolism and processing. While it plays a role in cellular processes, it is not considered a key driver or essential gene in cancer cell lines. Essential genes in cancer are typically those that regulate cell proliferation, survival, and metastasis, such as oncogenes and tumor suppressor genes. -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/ai_messages/baseline--DSPP: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: unlikely 3 | explanation: DSPP (Dentin Sialophosphoprotein) is primarily involved in tooth development and mineralization. It is not commonly associated with cancer cell lines or tumorigenesis. While some studies have reported its expression in certain cancer types, it is not considered an essential gene for cancer cell survival or proliferation. -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/ai_messages/baseline--EIF2B3: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: unlikely 3 | explanation: EIF2B3 (Eukaryotic Translation Initiation Factor 2B Subunit 3) is involved in the regulation of protein synthesis, specifically in the initiation phase. While it plays a role in cellular processes, it is not commonly considered as an essential gene in cancer cell lines. Essential genes in cancer cell lines are typically those that are directly involved in cell proliferation, survival, and other hallmarks of cancer. However, the essentiality of a gene can vary depending on the specific cancer type and context. -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/ai_messages/baseline--FAM71C: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: unlikely 3 | explanation: FAM71C is not well-studied in the context of cancer, and there is limited evidence to suggest that it plays a significant role in cancer cell lines. More research is needed to determine its potential involvement in cancer biology. -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/ai_messages/baseline--GK2: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: unlikely 3 | explanation: The GK2 gene, also known as glycerol kinase 2, is not commonly reported as an essential gene in cancer cell lines. Essential genes in cancer cells are typically those involved in cell proliferation, survival, and other critical cellular processes. While GK2 plays a role in glycerol metabolism, it is not considered a key player in cancer cell survival or growth. However, it is important to note that the essentiality of a gene can vary depending on the specific cancer type and context. -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/ai_messages/baseline--GPR78: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: unlikely 3 | explanation: There is currently no strong evidence in the literature to suggest that GPR78 is essential in cancer cell-lines. However, it is important to note that the role of genes in cancer can be context-dependent and may vary across different types of cancer and cell lines. Further research is needed to determine the potential role of GPR78 in cancer cell-lines. -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/ai_messages/baseline--GTF3C1: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: unlikely 3 | explanation: GTF3C1 is a general transcription factor involved in RNA polymerase III-mediated transcription. While it plays a role in transcription, it is not specifically associated with cancer cell survival or proliferation. Therefore, it is unlikely to be essential in cancer cell-lines. -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/ai_messages/baseline--H2BC1: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: unlikely 3 | explanation: The H2BC1 gene encodes a histone protein, which is involved in the packaging of DNA into chromatin. While histone proteins play important roles in regulating gene expression, they are not typically considered essential for cancer cell survival. Essential genes in cancer cell lines are more likely to be involved in processes such as cell proliferation, survival, and metabolism. However, it is important to note that the essentiality of a gene can be context-dependent and may vary among different cancer types and cell lines. -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/ai_messages/baseline--HAUS4: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: unlikely 3 | explanation: The HAUS4 gene is a part of the Augmin complex, which is involved in microtubule regulation during mitosis. While it plays a role in cell division, there is no strong evidence to suggest that it is specifically essential in cancer cell-lines. However, further research is needed to confirm its role in cancer development and progression. -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/ai_messages/baseline--HNRNPK: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: likely 3 | explanation: The HNRNPK gene encodes for heterogeneous nuclear ribonucleoprotein K, which is involved in various cellular processes such as transcription, translation, and signal transduction. It has been implicated in the development and progression of various cancers, including leukemia, breast cancer, and lung cancer. Its essential role in cancer cell lines is supported by its involvement in multiple cancer-related pathways and its association with poor prognosis in some cancer types. -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/ai_messages/baseline--HNRNPL: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: likely 3 | explanation: The HNRNPL gene encodes for heterogeneous nuclear ribonucleoprotein L, which is involved in pre-mRNA processing, alternative splicing, and mRNA stability. Dysregulation of these processes has been implicated in cancer development and progression. Additionally, several studies have reported the association of HNRNPL with various cancer types, suggesting that it may play a role in cancer cell survival and proliferation. Therefore, it is likely that HNRNPL is essential in cancer cell-lines. -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/ai_messages/baseline--HSPD1: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: likely 3 | explanation: The HSPD1 gene encodes for the heat shock protein 60 (HSP60), which is involved in protein folding and mitochondrial protein import. HSP60 has been implicated in cancer cell survival, proliferation, and metastasis. Its overexpression has been observed in various cancer types, and it has been suggested to play a role in cancer cell resistance to apoptosis. Therefore, HSPD1 is likely essential in cancer cell-lines. -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/ai_messages/baseline--IFNA10: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: unlikely 3 | explanation: IFNA10 (Interferon Alpha 10) is a type I interferon gene, which is involved in the immune response against viral infections and has some antiproliferative and antitumor activities. However, it is not considered an essential gene for the survival and proliferation of cancer cell lines. Essential genes in cancer cell lines are typically those that are critical for cell growth, division, and survival, such as oncogenes and tumor suppressor genes. While IFNA10 may play a role in the immune response against cancer, it is not likely essential for the survival and proliferation of cancer cell lines. -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/ai_messages/baseline--IL12B: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: unlikely 3 | explanation: The IL12B gene encodes a subunit of the cytokine interleukin-12, which is involved in the immune response and not directly in cell proliferation or survival. While it may play a role in the tumor microenvironment and immune response to cancer, it is not likely to be essential for the survival and growth of cancer cell lines themselves. -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/ai_messages/baseline--IL17F: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: unlikely 3 | explanation: IL17F is an interleukin gene involved in immune responses and inflammation. While it may play a role in the tumor microenvironment and influence cancer progression, it is not considered essential for the survival and proliferation of cancer cell lines. Essential genes in cancer cell lines are typically those involved in cell cycle regulation, DNA replication, and cell survival pathways. -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/ai_messages/baseline--KIF2B: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: likely 3 | explanation: KIF2B (Kinesin Family Member 2B) is a member of the kinesin-13 family, which plays a crucial role in microtubule dynamics and cell division. Abnormalities in microtubule dynamics and cell division are often associated with cancer development and progression. Therefore, KIF2B is likely essential in cancer cell-lines due to its involvement in these critical cellular processes. -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/ai_messages/baseline--KRT25: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: unlikely 3 | explanation: KRT25 (Keratin 25) is a type II cytokeratin, primarily expressed in hair follicles and associated with hair structure. It is not commonly associated with cancer cell lines or tumorigenesis. Essential genes in cancer cell lines are typically involved in cell proliferation, survival, and other critical cellular processes. Therefore, it is unlikely that KRT25 is essential in cancer cell-lines. -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/ai_messages/baseline--KRT26: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: unlikely 3 | explanation: KRT26 (Keratin 26) is a type II keratin gene, primarily expressed in hair follicles and not typically associated with cancer cell lines. Essential genes in cancer cell lines are usually involved in cell proliferation, survival, and other critical cellular processes. Therefore, it is unlikely that KRT26 is essential in cancer cell lines. -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/ai_messages/baseline--KRT82: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: unlikely 3 | explanation: KRT82 (Keratin 82) is a type II keratin protein primarily expressed in the hair follicle. It is not commonly associated with essential functions in cancer cell lines. Essential genes in cancer cell lines are typically involved in cell proliferation, survival, and other critical cellular processes. While KRT82 may be expressed in some cancer cells, it is unlikely to be essential for their survival or growth. -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/ai_messages/baseline--KRTAP9-3: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: unlikely 3 | explanation: KRTAP9-3 (Keratin Associated Protein 9-3) is a hair keratin-associated protein involved in the formation of hair shafts. It is not typically associated with essential cellular processes in cancer cell lines. Cancer cell lines usually rely on genes involved in cell proliferation, survival, and metastasis, rather than hair-related proteins. -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/ai_messages/baseline--LGALS14: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: unlikely 3 | explanation: The gene LGALS14 is not well-known for its involvement in cancer cell lines. While galectins, a family of proteins to which LGALS14 belongs, have been implicated in various cancer-related processes, there is limited evidence specifically linking LGALS14 to cancer cell lines. Therefore, it is unlikely that LGALS14 is essential in cancer cell-lines. -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/ai_messages/baseline--MRGPRX2: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: unlikely 3 | explanation: The MRGPRX2 gene encodes for a G protein-coupled receptor that is primarily expressed in mast cells and is involved in the regulation of allergic and inflammatory responses. There is limited evidence to suggest that MRGPRX2 plays a significant role in cancer cell lines, making it unlikely to be essential in cancer cell-lines. -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/ai_messages/baseline--NDST4: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: unlikely 3 | explanation: NDST4 (N-deacetylase/N-sulfotransferase 4) is not a well-known cancer-related gene, and there is limited evidence to suggest that it plays a significant role in cancer cell lines. While it may have some involvement in certain cellular processes, it is unlikely to be essential for cancer cell survival or proliferation. -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/ai_messages/baseline--NDUFB3: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: unlikely 3 | explanation: NDUFB3 is a subunit of the mitochondrial complex I, which is involved in oxidative phosphorylation. While it plays a role in cellular energy production, it is not directly associated with cancer cell survival or proliferation. Therefore, it is unlikely to be essential in cancer cell-lines. However, it is important to note that the essentiality of a gene can vary depending on the specific cancer type and context. -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/ai_messages/baseline--NOC3L: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: unlikely 3 | explanation: The NOC3L gene is not widely recognized as a cancer-related gene or an essential gene in cancer cell lines. While it may have some roles in cellular processes, there is insufficient evidence to suggest that it is essential for the survival or proliferation of cancer cells. -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/ai_messages/baseline--OPA1: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: unlikely 3 | explanation: OPA1 is primarily involved in mitochondrial fusion and maintenance of mitochondrial integrity. While it may have indirect effects on cancer cell survival and metabolism, it is not considered a key driver or essential gene in cancer cell lines. Essential genes in cancer are typically those that regulate cell proliferation, survival, and metastasis, such as oncogenes and tumor suppressor genes. -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/ai_messages/baseline--OR3A3: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: unlikely 3 | explanation: The OR3A3 gene encodes an olfactory receptor protein, which is primarily involved in the detection of odorants in the olfactory system. It is not known to play a significant role in cancer cell proliferation, survival, or other essential processes in cancer cell-lines. Therefore, it is unlikely that the OR3A3 gene is essential in cancer cell-lines. -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/ai_messages/baseline--OR52W1: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: unlikely 3 | explanation: OR52W1 is an olfactory receptor gene, which is primarily involved in the detection of odor molecules. It is not typically associated with essential cellular processes or pathways that are critical for cancer cell survival and proliferation. Therefore, it is unlikely that OR52W1 is essential in cancer cell-lines. -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/ai_messages/baseline--OR5T2: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: unlikely 3 | explanation: The OR5T2 gene encodes an olfactory receptor protein, which is primarily involved in the detection of odor molecules in the olfactory system. It is not known to play a significant role in cancer cell growth, survival, or proliferation. Therefore, it is unlikely that the OR5T2 gene is essential in cancer cell-lines. -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/ai_messages/baseline--POLR3H: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: unlikely 3 | explanation: POLR3H encodes a subunit of RNA polymerase III, which is involved in the transcription of small non-coding RNAs. While it plays a role in cellular processes, there is limited evidence to suggest that POLR3H is essential in cancer cell-lines. Most cancer-related studies focus on other genes with more direct roles in cell proliferation, survival, and metastasis. -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/ai_messages/baseline--PRPF19: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: likely 3 | explanation: PRPF19 is involved in multiple cellular processes, including pre-mRNA splicing, DNA repair, and the ubiquitin-proteasome system. Its dysregulation has been associated with various cancers, and it has been shown to be essential for the survival of certain cancer cell lines. Therefore, it is likely that PRPF19 is essential in cancer cell-lines. -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/ai_messages/baseline--PSMA7: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: unlikely 3 | explanation: PSMA7 (Proteasome Subunit Alpha 7) is a component of the proteasome complex, which is involved in protein degradation and cellular homeostasis. While proteasome function is important for cell survival, PSMA7 itself is not specifically associated with cancer cell lines or considered an essential gene for their survival. However, it is worth noting that the proteasome is a target for cancer therapy, such as the drug bortezomib, which inhibits proteasome function and induces apoptosis in cancer cells. -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/ai_messages/baseline--PSMB7: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: unlikely 3 | explanation: PSMB7 (Proteasome Subunit Beta 7) is a component of the 20S proteasome, which is involved in protein degradation. While the proteasome plays a role in maintaining cellular homeostasis, it is not specifically essential for cancer cell survival. However, cancer cells may have increased proteasome activity to cope with higher protein turnover. Inhibiting the proteasome can be a therapeutic strategy for some cancers, but PSMB7 itself is not likely essential in cancer cell-lines. -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/ai_messages/baseline--RBX1: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: likely 3 | explanation: RBX1 (Ring-Box 1) is a component of the SCF (Skp1-Cul1-F-box protein) E3 ubiquitin ligase complex, which plays a crucial role in the ubiquitin-proteasome pathway. This pathway is involved in the degradation of key regulatory proteins, including those that control cell cycle progression and apoptosis. Dysregulation of the ubiquitin-proteasome pathway has been implicated in the development and progression of various cancers. Therefore, RBX1 is likely essential in cancer cell-lines due to its involvement in the regulation of critical cellular processes. -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/ai_messages/baseline--RPL27: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: unlikely 3 | explanation: RPL27 is a ribosomal protein gene, which is involved in protein synthesis. Although ribosomal proteins are essential for cell survival, they are not specifically essential for cancer cell-lines. Cancer cells may have increased protein synthesis demands, but RPL27 is not a direct driver of cancer development or progression. -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/ai_messages/baseline--RPL37A: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: likely 3 | explanation: The RPL37A gene encodes a ribosomal protein that is a component of the 60S subunit. Ribosomal proteins are essential for protein synthesis, which is crucial for cell growth and proliferation. Cancer cells typically have increased rates of protein synthesis to support their rapid growth and division. Therefore, it is likely that the RPL37A gene is essential in cancer cell-lines, as it plays a critical role in the protein synthesis machinery. -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/ai_messages/baseline--RPL7A: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: likely 3 | explanation: RPL7A is a ribosomal protein gene, which is involved in protein synthesis. Many ribosomal protein genes have been found to be essential in cancer cell lines due to their role in maintaining high levels of protein synthesis required for rapid cell proliferation. Additionally, some studies have reported the association of RPL7A with cancer progression and poor prognosis. Therefore, it is likely that RPL7A is essential in cancer cell lines. -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/ai_messages/baseline--S100A7A: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: unlikely 3 | explanation: The S100A7A gene, also known as psoriasin, is primarily associated with skin inflammation and psoriasis. While it may have some role in certain cancer types, it is not considered essential for the survival and proliferation of cancer cell lines in general. Essential genes in cancer cell lines are typically those involved in cell cycle regulation, DNA repair, and apoptosis. -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/ai_messages/baseline--SDR9C7: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: unlikely 3 | explanation: The SDR9C7 gene encodes a short-chain dehydrogenase/reductase family 9C member 7 protein, which is involved in various metabolic processes. However, there is limited evidence to suggest that it plays a crucial role in cancer cell lines. While it may have some involvement in certain cancer types, it is not considered an essential gene for the survival and proliferation of cancer cells in general. -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/ai_messages/baseline--SLC25A2: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: unlikely 3 | explanation: The SLC25A2 gene encodes a mitochondrial solute carrier protein involved in the transport of inorganic phosphate. While it plays a role in cellular metabolism, there is limited evidence to suggest that it is essential for cancer cell survival or proliferation. Other genes, such as oncogenes and tumor suppressor genes, have a more direct impact on cancer cell growth and are more likely to be essential in cancer cell-lines. -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/ai_messages/baseline--SS18L2: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: unlikely 3 | explanation: SS18L2 (also known as CREST) is not a well-known essential gene in cancer cell lines. While it has been implicated in some cancer-related processes, such as chromosomal translocations in synovial sarcoma, it is not considered a universally essential gene for cancer cell survival and proliferation. Essential genes in cancer cell lines are typically those involved in fundamental cellular processes, such as DNA replication, cell division, and metabolism. -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/ai_messages/baseline--SUPT16H: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: likely 3 | explanation: The SUPT16H gene encodes a subunit of the FACT complex, which is involved in chromatin remodeling and transcription regulation. It has been implicated in the survival and proliferation of cancer cells, making it likely essential in cancer cell-lines. -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/ai_messages/baseline--TIMM29: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: unlikely 3 | explanation: TIMM29 (Translocase of Inner Mitochondrial Membrane 29) is involved in the import and insertion of proteins into the mitochondrial inner membrane. While it plays a role in mitochondrial function, there is no strong evidence to suggest that it is essential in cancer cell-lines. Cancer cells often have altered metabolism and mitochondrial function, but the essentiality of TIMM29 in cancer cell-lines is not well-established. -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/ai_messages/baseline--TMPRSS11F: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: unlikely 3 | explanation: The gene TMPRSS11F (Transmembrane Serine Protease 11F) has not been widely reported as essential in cancer cell lines. While it may have some roles in specific cancer types, it is not considered a broadly essential gene across various cancer cell lines. More research would be needed to determine its specific role in certain cancer types. -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/ai_messages/baseline--ZP2: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: unlikely 3 | explanation: ZP2 (Zona Pellucida Glycoprotein 2) is primarily involved in the formation of the zona pellucida, an extracellular matrix surrounding oocytes in mammals. Its function is related to fertilization and early embryo development, rather than cancer cell growth or survival. Therefore, it is unlikely that ZP2 is essential in cancer cell-lines. -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/prompts/AGAP5: -------------------------------------------------------------------------------- 1 | Is the gene AGAP5 likely essential in cancer cell-lines? Provide an outcome of either "likely" or "unlikely". 2 | -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/prompts/ANKLE2: -------------------------------------------------------------------------------- 1 | Is the gene ANKLE2 likely essential in cancer cell-lines? Provide an outcome of either "likely" or "unlikely". 2 | -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/prompts/AQP8: -------------------------------------------------------------------------------- 1 | Is the gene AQP8 likely essential in cancer cell-lines? Provide an outcome of either "likely" or "unlikely". 2 | -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/prompts/CCT5: -------------------------------------------------------------------------------- 1 | Is the gene CCT5 likely essential in cancer cell-lines? Provide an outcome of either "likely" or "unlikely". 2 | -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/prompts/DCTN3: -------------------------------------------------------------------------------- 1 | Is the gene DCTN3 likely essential in cancer cell-lines? Provide an outcome of either "likely" or "unlikely". 2 | -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/prompts/DHX16: -------------------------------------------------------------------------------- 1 | Is the gene DHX16 likely essential in cancer cell-lines? Provide an outcome of either "likely" or "unlikely". 2 | -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/prompts/DSPP: -------------------------------------------------------------------------------- 1 | Is the gene DSPP likely essential in cancer cell-lines? Provide an outcome of either "likely" or "unlikely". 2 | -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/prompts/EIF2B3: -------------------------------------------------------------------------------- 1 | Is the gene EIF2B3 likely essential in cancer cell-lines? Provide an outcome of either "likely" or "unlikely". 2 | -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/prompts/FAM71C: -------------------------------------------------------------------------------- 1 | Is the gene FAM71C likely essential in cancer cell-lines? Provide an outcome of either "likely" or "unlikely". 2 | -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/prompts/GK2: -------------------------------------------------------------------------------- 1 | Is the gene GK2 likely essential in cancer cell-lines? Provide an outcome of either "likely" or "unlikely". 2 | -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/prompts/GPR78: -------------------------------------------------------------------------------- 1 | Is the gene GPR78 likely essential in cancer cell-lines? Provide an outcome of either "likely" or "unlikely". 2 | -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/prompts/GTF3C1: -------------------------------------------------------------------------------- 1 | Is the gene GTF3C1 likely essential in cancer cell-lines? Provide an outcome of either "likely" or "unlikely". 2 | -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/prompts/H2BC1: -------------------------------------------------------------------------------- 1 | Is the gene H2BC1 likely essential in cancer cell-lines? Provide an outcome of either "likely" or "unlikely". 2 | -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/prompts/HAUS4: -------------------------------------------------------------------------------- 1 | Is the gene HAUS4 likely essential in cancer cell-lines? Provide an outcome of either "likely" or "unlikely". 2 | -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/prompts/HNRNPK: -------------------------------------------------------------------------------- 1 | Is the gene HNRNPK likely essential in cancer cell-lines? Provide an outcome of either "likely" or "unlikely". 2 | -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/prompts/HNRNPL: -------------------------------------------------------------------------------- 1 | Is the gene HNRNPL likely essential in cancer cell-lines? Provide an outcome of either "likely" or "unlikely". 2 | -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/prompts/HSPD1: -------------------------------------------------------------------------------- 1 | Is the gene HSPD1 likely essential in cancer cell-lines? Provide an outcome of either "likely" or "unlikely". 2 | -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/prompts/IFNA10: -------------------------------------------------------------------------------- 1 | Is the gene IFNA10 likely essential in cancer cell-lines? Provide an outcome of either "likely" or "unlikely". 2 | -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/prompts/IL12B: -------------------------------------------------------------------------------- 1 | Is the gene IL12B likely essential in cancer cell-lines? Provide an outcome of either "likely" or "unlikely". 2 | -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/prompts/IL17F: -------------------------------------------------------------------------------- 1 | Is the gene IL17F likely essential in cancer cell-lines? Provide an outcome of either "likely" or "unlikely". 2 | -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/prompts/KIF2B: -------------------------------------------------------------------------------- 1 | Is the gene KIF2B likely essential in cancer cell-lines? Provide an outcome of either "likely" or "unlikely". 2 | -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/prompts/KRT25: -------------------------------------------------------------------------------- 1 | Is the gene KRT25 likely essential in cancer cell-lines? Provide an outcome of either "likely" or "unlikely". 2 | -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/prompts/KRT26: -------------------------------------------------------------------------------- 1 | Is the gene KRT26 likely essential in cancer cell-lines? Provide an outcome of either "likely" or "unlikely". 2 | -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/prompts/KRT82: -------------------------------------------------------------------------------- 1 | Is the gene KRT82 likely essential in cancer cell-lines? Provide an outcome of either "likely" or "unlikely". 2 | -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/prompts/KRTAP9-3: -------------------------------------------------------------------------------- 1 | Is the gene KRTAP9-3 likely essential in cancer cell-lines? Provide an outcome of either "likely" or "unlikely". 2 | -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/prompts/LGALS14: -------------------------------------------------------------------------------- 1 | Is the gene LGALS14 likely essential in cancer cell-lines? Provide an outcome of either "likely" or "unlikely". 2 | -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/prompts/MRGPRX2: -------------------------------------------------------------------------------- 1 | Is the gene MRGPRX2 likely essential in cancer cell-lines? Provide an outcome of either "likely" or "unlikely". 2 | -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/prompts/NDST4: -------------------------------------------------------------------------------- 1 | Is the gene NDST4 likely essential in cancer cell-lines? Provide an outcome of either "likely" or "unlikely". 2 | -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/prompts/NDUFB3: -------------------------------------------------------------------------------- 1 | Is the gene NDUFB3 likely essential in cancer cell-lines? Provide an outcome of either "likely" or "unlikely". 2 | -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/prompts/NOC3L: -------------------------------------------------------------------------------- 1 | Is the gene NOC3L likely essential in cancer cell-lines? Provide an outcome of either "likely" or "unlikely". 2 | -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/prompts/OPA1: -------------------------------------------------------------------------------- 1 | Is the gene OPA1 likely essential in cancer cell-lines? Provide an outcome of either "likely" or "unlikely". 2 | -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/prompts/OR3A3: -------------------------------------------------------------------------------- 1 | Is the gene OR3A3 likely essential in cancer cell-lines? Provide an outcome of either "likely" or "unlikely". 2 | -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/prompts/OR52W1: -------------------------------------------------------------------------------- 1 | Is the gene OR52W1 likely essential in cancer cell-lines? Provide an outcome of either "likely" or "unlikely". 2 | -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/prompts/OR5T2: -------------------------------------------------------------------------------- 1 | Is the gene OR5T2 likely essential in cancer cell-lines? Provide an outcome of either "likely" or "unlikely". 2 | -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/prompts/POLR3H: -------------------------------------------------------------------------------- 1 | Is the gene POLR3H likely essential in cancer cell-lines? Provide an outcome of either "likely" or "unlikely". 2 | -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/prompts/PRPF19: -------------------------------------------------------------------------------- 1 | Is the gene PRPF19 likely essential in cancer cell-lines? Provide an outcome of either "likely" or "unlikely". 2 | -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/prompts/PSMA7: -------------------------------------------------------------------------------- 1 | Is the gene PSMA7 likely essential in cancer cell-lines? Provide an outcome of either "likely" or "unlikely". 2 | -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/prompts/PSMB7: -------------------------------------------------------------------------------- 1 | Is the gene PSMB7 likely essential in cancer cell-lines? Provide an outcome of either "likely" or "unlikely". 2 | -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/prompts/RBX1: -------------------------------------------------------------------------------- 1 | Is the gene RBX1 likely essential in cancer cell-lines? Provide an outcome of either "likely" or "unlikely". 2 | -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/prompts/RPL27: -------------------------------------------------------------------------------- 1 | Is the gene RPL27 likely essential in cancer cell-lines? Provide an outcome of either "likely" or "unlikely". 2 | -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/prompts/RPL37A: -------------------------------------------------------------------------------- 1 | Is the gene RPL37A likely essential in cancer cell-lines? Provide an outcome of either "likely" or "unlikely". 2 | -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/prompts/RPL7A: -------------------------------------------------------------------------------- 1 | Is the gene RPL7A likely essential in cancer cell-lines? Provide an outcome of either "likely" or "unlikely". 2 | -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/prompts/S100A7A: -------------------------------------------------------------------------------- 1 | Is the gene S100A7A likely essential in cancer cell-lines? Provide an outcome of either "likely" or "unlikely". 2 | -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/prompts/SDR9C7: -------------------------------------------------------------------------------- 1 | Is the gene SDR9C7 likely essential in cancer cell-lines? Provide an outcome of either "likely" or "unlikely". 2 | -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/prompts/SLC25A2: -------------------------------------------------------------------------------- 1 | Is the gene SLC25A2 likely essential in cancer cell-lines? Provide an outcome of either "likely" or "unlikely". 2 | -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/prompts/SS18L2: -------------------------------------------------------------------------------- 1 | Is the gene SS18L2 likely essential in cancer cell-lines? Provide an outcome of either "likely" or "unlikely". 2 | -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/prompts/SUPT16H: -------------------------------------------------------------------------------- 1 | Is the gene SUPT16H likely essential in cancer cell-lines? Provide an outcome of either "likely" or "unlikely". 2 | -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/prompts/TIMM29: -------------------------------------------------------------------------------- 1 | Is the gene TIMM29 likely essential in cancer cell-lines? Provide an outcome of either "likely" or "unlikely". 2 | -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/prompts/TMPRSS11F: -------------------------------------------------------------------------------- 1 | Is the gene TMPRSS11F likely essential in cancer cell-lines? Provide an outcome of either "likely" or "unlikely". 2 | -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/prompts/ZP2: -------------------------------------------------------------------------------- 1 | Is the gene ZP2 likely essential in cancer cell-lines? Provide an outcome of either "likely" or "unlikely". 2 | -------------------------------------------------------------------------------- /experiments/gene_essentiality_cancer/src/gene_essentiality_cancer_createRandomGeneSubset.R: -------------------------------------------------------------------------------- 1 | source("main_fxns.R") 2 | library(R.utils) 3 | experimentSel = 'gene_essentiality_cancer_proliferation' 4 | set.seed(42) 5 | nGenesPerCat = 25 6 | 7 | essentialGeneFile = file.path('..','data',experimentSel,'CRISPRInferredCommonEssentials.csv') 8 | essentialGene.vect = loadGeneVect(essentialGeneFile) 9 | essentialGene.vect.subset = sample(essentialGene.vect,nGenesPerCat,replace=FALSE) 10 | 11 | nonEssentialGeneFile = file.path('..','data',experimentSel,'AchillesNonessentialControls.csv') 12 | nonEssentialGene.vect = loadGeneVect(nonEssentialGeneFile) 13 | nonEssentialGene.vect.subset = sample(nonEssentialGene.vect,nGenesPerCat,replace=FALSE) 14 | 15 | folderForFiles=file.path('..','experiments',experimentSel,'prompts') 16 | mkdirs(folderForFiles) 17 | for (geneSel in c(essentialGene.vect.subset,nonEssentialGene.vect.subset)){ 18 | promptSel = paste0('Is the gene ',geneSel,' likely essential in cancer cell-lines? Provide an outcome of either "likely" or "unlikely".') 19 | writeLines(text = promptSel, con = file.path(folderForFiles,geneSel)) 20 | } -------------------------------------------------------------------------------- /experiments/glioblastoma_survival/ai_messages/baseline--generated_EGFR_MGMT_low_methylated: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: 3 | survival_relative_to_median: 'decreased' 4 | survival_deviation_months: -4 5 | explanation: The patient has a glioblastoma with an EGFR mutation and low MGMT methylation status. EGFR mutations are associated with a more aggressive tumor phenotype, while low MGMT methylation status reduces the effectiveness of standard chemotherapy (temozolomide). These factors contribute to a decreased survival relative to the median overall glioblastoma survival, with an estimated deviation of 4 months. -------------------------------------------------------------------------------- /experiments/glioblastoma_survival/ai_messages/baseline--generated_NF1_MGMT_medium_methylated: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: 3 | survival_relative_to_median: increased 4 | survival_deviation_months: 3 5 | explanation: The patient has a mutation in the NF1 gene and medium MGMT methylation status. While NF1 mutations are associated with poorer prognosis, medium MGMT methylation status suggests a better response to temozolomide chemotherapy, which is part of the standard of care for glioblastoma. The absence of other detrimental mutations (e.g., EGFR, TP53, PIK3CA, MUC16, PTEN, and RB1) may also contribute to a slightly better outcome. Therefore, the patient's survival is more likely to be increased relative to the median overall glioblastoma survival (15 months) by an estimated 3 months. -------------------------------------------------------------------------------- /experiments/glioblastoma_survival/ai_messages/baseline--generated_PIK3CA_MGMT_high_methylated: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: 3 | survival_relative_to_median: increased 4 | survival_deviation_months: 6 5 | explanation: The patient's glioblastoma has a PIK3CA mutation and high MGMT methylation status. The high MGMT methylation status is associated with a better response to temozolomide, a standard chemotherapy drug for glioblastoma, which may lead to increased survival. The PIK3CA mutation may be targetable with genotype-informed treatment, such as PI3K inhibitors, which could further improve the patient's prognosis. However, the absence of mutations in other genes like EGFR, TP53, NF1, MUC16, PTEN, and RB1 reduces the number of potential targeted therapies. Based on these factors, the patient's survival is more likely to be increased relative to the median overall glioblastoma survival (15 months) by an estimated 6 months. -------------------------------------------------------------------------------- /experiments/glioblastoma_survival/ai_messages/baseline--generated_PTEN_EGFR_MGMT_high_methylated: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: 3 | survival_relative_to_median: increased 4 | survival_deviation_months: 6 5 | explanation: The patient has mutations in PTEN and EGFR, which are commonly associated with glioblastoma. However, the high MGMT methylation status suggests a better response to temozolomide, a standard-of-care chemotherapy drug. This factor may increase the patient's survival relative to the median overall glioblastoma survival. The absence of mutations in TP53, PIK3CA, NF1, MUC16, and RB1 may also contribute to a more favorable prognosis. Based on these factors, the patient's survival is estimated to be increased by 6 months relative to the median survival of 15 months. -------------------------------------------------------------------------------- /experiments/glioblastoma_survival/ai_messages/baseline--generated_PTEN_MGMT_medium_methylated: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: 3 | survival_relative_to_median: increased 4 | survival_deviation: 3 5 | explanation: The patient has a PTEN mutation and medium MGMT methylation status. PTEN mutations are associated with a worse prognosis, but medium MGMT methylation status suggests a better response to temozolomide chemotherapy, which is part of the standard of care for glioblastoma. The absence of other mutations, such as EGFR, TP53, PIK3CA, NF1, MUC16, and RB1, may also contribute to a slightly better outcome. Overall, the patient's survival is likely to be increased relative to the median glioblastoma survival by approximately 3 months. -------------------------------------------------------------------------------- /experiments/glioblastoma_survival/ai_messages/baseline--generated_PTEN_MUC16_MGMT_medium_methylated: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: 3 | survival_relative_to_median: increased 4 | survival_deviation_months: 3 5 | explanation: The presence of PTEN and MUC16 mutations may contribute to tumor progression, but the medium MGMT methylation status suggests a better response to temozolomide chemotherapy, which is part of the standard of care for glioblastoma. The absence of mutations in EGFR, TP53, PIK3CA, NF1, and RB1 may also be favorable for the patient. Considering these factors, the patient's survival is more likely to be increased relative to the median overall glioblastoma survival (15 months) by an estimated 3 months. -------------------------------------------------------------------------------- /experiments/glioblastoma_survival/ai_messages/baseline--generated_PTEN_NF1_MGMT_low_methylated: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: 3 | survival_relative_to_median: 'decreased' 4 | survival_deviation_months: -4 5 | explanation: The presence of PTEN and NF1 mutations in the glioblastoma may lead to a more aggressive tumor and poorer prognosis. Additionally, low MGMT methylation status is associated with reduced responsiveness to temozolomide, a standard chemotherapy for glioblastoma. These factors combined suggest a decreased survival relative to the median overall glioblastoma survival, with an estimated deviation of 4 months. -------------------------------------------------------------------------------- /experiments/glioblastoma_survival/ai_messages/baseline--generated_PTEN_TP53_NF1_RB1_MGMT_medium_methylated: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: 3 | survival_relative_to_median: 'decreased' 4 | survival_deviation_months: -4 5 | explanation: The presence of mutations in PTEN, TP53, NF1, and RB1 genes are associated with a more aggressive glioblastoma phenotype and poorer prognosis. Although MGMT methylation status is medium, which could potentially improve the response to temozolomide treatment, the overall impact of the identified mutations is likely to result in a decreased survival relative to the median overall glioblastoma survival. Based on the available information, we estimate a decrease of 4 months from the median survival of 15 months. -------------------------------------------------------------------------------- /experiments/glioblastoma_survival/ai_messages/baseline--generated_TP53_MGMT_medium_methylated: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: 3 | survival_relative_to_median: increased 4 | survival_deviation_months: 3 5 | explanation: The patient has a TP53 mutation and medium MGMT methylation status. TP53 mutations are common in glioblastomas and do not significantly impact survival. However, medium MGMT methylation status is associated with a better response to temozolomide, a standard-of-care chemotherapy drug. This improved response may lead to a modest increase in overall survival compared to the median glioblastoma survival of 15 months. -------------------------------------------------------------------------------- /experiments/glioblastoma_survival/ai_messages/baseline--generated_TP53_PIK3CA_MGMT_medium_methylated: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: 3 | survival_relative_to_median: increased 4 | survival_deviation: 3 5 | explanation: The presence of TP53 and PIK3CA mutations may contribute to tumor progression, but the medium MGMT methylation status suggests a better response to temozolomide, a standard-of-care chemotherapy for glioblastoma. This could lead to a modest increase in overall survival relative to the median. The absence of mutations in other genes such as EGFR, NF1, MUC16, PTEN, and RB1 may also contribute to a slightly better prognosis. -------------------------------------------------------------------------------- /experiments/glioblastoma_survival/data/means.csv: -------------------------------------------------------------------------------- 1 | ,count,mean,std,min,25%,50%,75%,max,methylation 2 | "('PTEN', 'MGMT_medium_methylated')",9.0,174.0,278.0202330766594,6.0,19.0,66.0,192.0,873.0,MGMT_medium_methylated 3 | "('TP53', 'MGMT_medium_methylated')",6.0,755.1666666666666,541.1781284075205,121.0,449.0,740.5,840.75,1703.0,MGMT_medium_methylated 4 | "('EGFR', 'MGMT_low_methylated')",4.0,390.25,256.3297030518833,7.0,374.5,504.0,519.75,546.0,MGMT_low_methylated 5 | "('PTEN', 'EGFR', 'MGMT_high_methylated')",4.0,651.25,355.77368742877354,291.0,431.25,599.5,819.5,1115.0,MGMT_high_methylated 6 | "('NF1', 'MGMT_medium_methylated')",4.0,245.25,132.86427912221805,116.0,139.25,243.5,349.5,378.0,MGMT_medium_methylated 7 | "('PTEN', 'NF1', 'MGMT_low_methylated')",3.0,328.3333333333333,191.83673614126502,127.0,238.0,349.0,429.0,509.0,MGMT_low_methylated 8 | "('PIK3CA', 'MGMT_high_methylated')",2.0,478.5,236.88077169749343,311.0,394.75,478.5,562.25,646.0,MGMT_high_methylated 9 | "('PTEN', 'MUC16', 'MGMT_medium_methylated')",2.0,188.0,108.89444430272832,111.0,149.5,188.0,226.5,265.0,MGMT_medium_methylated 10 | "('PTEN', 'TP53', 'NF1', 'RB1', 'MGMT_medium_methylated')",2.0,598.0,292.74220741123065,391.0,494.5,598.0,701.5,805.0,MGMT_medium_methylated 11 | "('TP53', 'PIK3CA', 'MGMT_medium_methylated')",2.0,385.5,290.62088706767105,180.0,282.75,385.5,488.25,591.0,MGMT_medium_methylated 12 | -------------------------------------------------------------------------------- /experiments/glioblastoma_survival/main.md: -------------------------------------------------------------------------------- 1 | # Combinatorial assessment of top-10 mutations in glioblastoma on survival 2 | 3 | See paper methods and code for details 4 | 5 | - main.py: generate prompts 6 | - analyze.py: analyze LLM results 7 | -------------------------------------------------------------------------------- /experiments/glioblastoma_survival/prompt_template: -------------------------------------------------------------------------------- 1 | Consider a patient diagnosed with a glioblastoma with genetic alterations, as indicated below. Explore the tumor progression under standard of care with genotype-informed treatment (if applicable). As final outcome, provide two values, first indicating whether the patient's survival is more likely to be 'increased' or 'decreased' relative to the median overall glioblastoma survival (15 months), and second, the number of months by which you estimate the survival to deviate from that median. Focus on novelty and ignore the inherent uncertainty of this scenario to provide bold estimates based on your knowledge. 2 | 3 | Identified mutations: 4 | {genes} 5 | -------------------------------------------------------------------------------- /experiments/glioblastoma_survival/prompts/generated_EGFR_MGMT_low_methylated: -------------------------------------------------------------------------------- 1 | Consider a patient diagnosed with a glioblastoma with genetic alterations, as indicated below. Explore the tumor progression under standard of care with genotype-informed treatment (if applicable). As final outcome, provide two values, first indicating whether the patient's survival is more likely to be 'increased' or 'decreased' relative to the median overall glioblastoma survival (15 months), and second, the number of months by which you estimate the survival to deviate from that median. Focus on novelty and ignore the inherent uncertainty of this scenario to provide bold estimates based on your knowledge. 2 | 3 | Identified mutations: 4 | EGFR: mutations found 5 | MGMT methylation status: low 6 | TP53: no mutations found 7 | PIK3CA: no mutations found 8 | NF1: no mutations found 9 | MUC16: no mutations found 10 | PTEN: no mutations found 11 | RB1: no mutations found 12 | -------------------------------------------------------------------------------- /experiments/glioblastoma_survival/prompts/generated_NF1_MGMT_medium_methylated: -------------------------------------------------------------------------------- 1 | Consider a patient diagnosed with a glioblastoma with genetic alterations, as indicated below. Explore the tumor progression under standard of care with genotype-informed treatment (if applicable). As final outcome, provide two values, first indicating whether the patient's survival is more likely to be 'increased' or 'decreased' relative to the median overall glioblastoma survival (15 months), and second, the number of months by which you estimate the survival to deviate from that median. Focus on novelty and ignore the inherent uncertainty of this scenario to provide bold estimates based on your knowledge. 2 | 3 | Identified mutations: 4 | NF1: mutations found 5 | MGMT methylation status: medium 6 | EGFR: no mutations found 7 | TP53: no mutations found 8 | PIK3CA: no mutations found 9 | MUC16: no mutations found 10 | PTEN: no mutations found 11 | RB1: no mutations found 12 | -------------------------------------------------------------------------------- /experiments/glioblastoma_survival/prompts/generated_PIK3CA_MGMT_high_methylated: -------------------------------------------------------------------------------- 1 | Consider a patient diagnosed with a glioblastoma with genetic alterations, as indicated below. Explore the tumor progression under standard of care with genotype-informed treatment (if applicable). As final outcome, provide two values, first indicating whether the patient's survival is more likely to be 'increased' or 'decreased' relative to the median overall glioblastoma survival (15 months), and second, the number of months by which you estimate the survival to deviate from that median. Focus on novelty and ignore the inherent uncertainty of this scenario to provide bold estimates based on your knowledge. 2 | 3 | Identified mutations: 4 | PIK3CA: mutations found 5 | MGMT methylation status: high 6 | EGFR: no mutations found 7 | TP53: no mutations found 8 | NF1: no mutations found 9 | MUC16: no mutations found 10 | PTEN: no mutations found 11 | RB1: no mutations found 12 | -------------------------------------------------------------------------------- /experiments/glioblastoma_survival/prompts/generated_PTEN_EGFR_MGMT_high_methylated: -------------------------------------------------------------------------------- 1 | Consider a patient diagnosed with a glioblastoma with genetic alterations, as indicated below. Explore the tumor progression under standard of care with genotype-informed treatment (if applicable). As final outcome, provide two values, first indicating whether the patient's survival is more likely to be 'increased' or 'decreased' relative to the median overall glioblastoma survival (15 months), and second, the number of months by which you estimate the survival to deviate from that median. Focus on novelty and ignore the inherent uncertainty of this scenario to provide bold estimates based on your knowledge. 2 | 3 | Identified mutations: 4 | PTEN: mutations found 5 | EGFR: mutations found 6 | MGMT methylation status: high 7 | TP53: no mutations found 8 | PIK3CA: no mutations found 9 | NF1: no mutations found 10 | MUC16: no mutations found 11 | RB1: no mutations found 12 | -------------------------------------------------------------------------------- /experiments/glioblastoma_survival/prompts/generated_PTEN_MGMT_medium_methylated: -------------------------------------------------------------------------------- 1 | Consider a patient diagnosed with a glioblastoma with genetic alterations, as indicated below. Explore the tumor progression under standard of care with genotype-informed treatment (if applicable). As final outcome, provide two values, first indicating whether the patient's survival is more likely to be 'increased' or 'decreased' relative to the median overall glioblastoma survival (15 months), and second, the number of months by which you estimate the survival to deviate from that median. Focus on novelty and ignore the inherent uncertainty of this scenario to provide bold estimates based on your knowledge. 2 | 3 | Identified mutations: 4 | PTEN: mutations found 5 | MGMT methylation status: medium 6 | EGFR: no mutations found 7 | TP53: no mutations found 8 | PIK3CA: no mutations found 9 | NF1: no mutations found 10 | MUC16: no mutations found 11 | RB1: no mutations found 12 | -------------------------------------------------------------------------------- /experiments/glioblastoma_survival/prompts/generated_PTEN_MUC16_MGMT_medium_methylated: -------------------------------------------------------------------------------- 1 | Consider a patient diagnosed with a glioblastoma with genetic alterations, as indicated below. Explore the tumor progression under standard of care with genotype-informed treatment (if applicable). As final outcome, provide two values, first indicating whether the patient's survival is more likely to be 'increased' or 'decreased' relative to the median overall glioblastoma survival (15 months), and second, the number of months by which you estimate the survival to deviate from that median. Focus on novelty and ignore the inherent uncertainty of this scenario to provide bold estimates based on your knowledge. 2 | 3 | Identified mutations: 4 | PTEN: mutations found 5 | MUC16: mutations found 6 | MGMT methylation status: medium 7 | EGFR: no mutations found 8 | TP53: no mutations found 9 | PIK3CA: no mutations found 10 | NF1: no mutations found 11 | RB1: no mutations found 12 | -------------------------------------------------------------------------------- /experiments/glioblastoma_survival/prompts/generated_PTEN_NF1_MGMT_low_methylated: -------------------------------------------------------------------------------- 1 | Consider a patient diagnosed with a glioblastoma with genetic alterations, as indicated below. Explore the tumor progression under standard of care with genotype-informed treatment (if applicable). As final outcome, provide two values, first indicating whether the patient's survival is more likely to be 'increased' or 'decreased' relative to the median overall glioblastoma survival (15 months), and second, the number of months by which you estimate the survival to deviate from that median. Focus on novelty and ignore the inherent uncertainty of this scenario to provide bold estimates based on your knowledge. 2 | 3 | Identified mutations: 4 | PTEN: mutations found 5 | NF1: mutations found 6 | MGMT methylation status: low 7 | EGFR: no mutations found 8 | TP53: no mutations found 9 | PIK3CA: no mutations found 10 | MUC16: no mutations found 11 | RB1: no mutations found 12 | -------------------------------------------------------------------------------- /experiments/glioblastoma_survival/prompts/generated_PTEN_TP53_NF1_RB1_MGMT_medium_methylated: -------------------------------------------------------------------------------- 1 | Consider a patient diagnosed with a glioblastoma with genetic alterations, as indicated below. Explore the tumor progression under standard of care with genotype-informed treatment (if applicable). As final outcome, provide two values, first indicating whether the patient's survival is more likely to be 'increased' or 'decreased' relative to the median overall glioblastoma survival (15 months), and second, the number of months by which you estimate the survival to deviate from that median. Focus on novelty and ignore the inherent uncertainty of this scenario to provide bold estimates based on your knowledge. 2 | 3 | Identified mutations: 4 | PTEN: mutations found 5 | TP53: mutations found 6 | NF1: mutations found 7 | RB1: mutations found 8 | MGMT methylation status: medium 9 | MUC16: no mutations found 10 | EGFR: no mutations found 11 | PIK3CA: no mutations found 12 | -------------------------------------------------------------------------------- /experiments/glioblastoma_survival/prompts/generated_TP53_MGMT_medium_methylated: -------------------------------------------------------------------------------- 1 | Consider a patient diagnosed with a glioblastoma with genetic alterations, as indicated below. Explore the tumor progression under standard of care with genotype-informed treatment (if applicable). As final outcome, provide two values, first indicating whether the patient's survival is more likely to be 'increased' or 'decreased' relative to the median overall glioblastoma survival (15 months), and second, the number of months by which you estimate the survival to deviate from that median. Focus on novelty and ignore the inherent uncertainty of this scenario to provide bold estimates based on your knowledge. 2 | 3 | Identified mutations: 4 | TP53: mutations found 5 | MGMT methylation status: medium 6 | EGFR: no mutations found 7 | PIK3CA: no mutations found 8 | NF1: no mutations found 9 | MUC16: no mutations found 10 | PTEN: no mutations found 11 | RB1: no mutations found 12 | -------------------------------------------------------------------------------- /experiments/glioblastoma_survival/prompts/generated_TP53_PIK3CA_MGMT_medium_methylated: -------------------------------------------------------------------------------- 1 | Consider a patient diagnosed with a glioblastoma with genetic alterations, as indicated below. Explore the tumor progression under standard of care with genotype-informed treatment (if applicable). As final outcome, provide two values, first indicating whether the patient's survival is more likely to be 'increased' or 'decreased' relative to the median overall glioblastoma survival (15 months), and second, the number of months by which you estimate the survival to deviate from that median. Focus on novelty and ignore the inherent uncertainty of this scenario to provide bold estimates based on your knowledge. 2 | 3 | Identified mutations: 4 | TP53: mutations found 5 | PIK3CA: mutations found 6 | MGMT methylation status: medium 7 | EGFR: no mutations found 8 | NF1: no mutations found 9 | MUC16: no mutations found 10 | PTEN: no mutations found 11 | RB1: no mutations found 12 | -------------------------------------------------------------------------------- /experiments/mouse_immunology/ai_messages/baseline--Cyanide: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: Death 3 | explanation: Cyanide is a potent toxin that inhibits cellular respiration by binding to cytochrome c oxidase, a key enzyme in the electron transport chain. This prevents cells from producing ATP, leading to a rapid depletion of energy and ultimately causing death in the organism. -------------------------------------------------------------------------------- /experiments/mouse_immunology/ai_messages/baseline--YUMM_1_7: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: Tumor growth 3 | explanation: YUMM 1.7 is a mouse melanoma cell line derived from a genetically engineered mouse model. When injected into a wild type mouse, these cells can form tumors, leading to tumor growth as the most relevant final outcome. -------------------------------------------------------------------------------- /experiments/mouse_immunology/prompts/Cyanide: -------------------------------------------------------------------------------- 1 | A wild type mouse is injected with Cyanide. Report the most relevant final outcome. -------------------------------------------------------------------------------- /experiments/mouse_immunology/prompts/YUMM_1_7: -------------------------------------------------------------------------------- 1 | A wild type mouse is injected with YUMM 1.7. Report the most relevant final outcome. -------------------------------------------------------------------------------- /experiments/sepsis_treatment/ai_messages/baseline--sepsis_hyperinflammation: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: Broad-spectrum antibiotics, fluid resuscitation, and monitoring of immune response 3 | explanation: The patient has sepsis, which is typically treated with broad-spectrum antibiotics to target the underlying infection. Fluid resuscitation is also important to maintain blood pressure and organ perfusion. High ferritin levels indicate an inflammatory response, and a high percentage of CD45/CD14-monocytes expressing HLA-DR suggests an active immune response. Monitoring the patient's immune response and adjusting treatment as needed is crucial for optimal recovery. -------------------------------------------------------------------------------- /experiments/sepsis_treatment/ai_messages/baseline--sepsis_immunoparalysis: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: Administration of intravenous antibiotics, fluids, and possible use of corticosteroids. 3 | explanation: The patient has sepsis, which is a life-threatening condition caused by the body's response to an infection. Low ferritin levels indicate a possible iron deficiency, while a low percentage of CD45/CD14-monocytes expressing HLA-DR suggests an impaired immune response. The recommended treatment for sepsis includes intravenous antibiotics to target the underlying infection, fluids to maintain blood pressure and organ function, and corticosteroids may be considered to modulate the immune response. -------------------------------------------------------------------------------- /experiments/sepsis_treatment/prompts/sepsis_hyperinflammation: -------------------------------------------------------------------------------- 1 | A patient with sepsis has high ferritin levels and a high percentage of CD45/CD14-monocytes that express HLA-DR. What treatment would be recommended? -------------------------------------------------------------------------------- /experiments/sepsis_treatment/prompts/sepsis_immunoparalysis: -------------------------------------------------------------------------------- 1 | A patient with sepsis has low ferritin levels and a low percentage of CD45/CD14-monocytes that express HLA-DR. What treatment would be recommended? -------------------------------------------------------------------------------- /experiments/trained_immunity/ai_messages/baseline--tolerance_LPS: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: Tolerance to high-dose LPS 3 | explanation: Upon rechallenge with high-dose LPS after 90 days, the mouse is likely to exhibit tolerance to LPS. This is because the initial exposure to high-dose LPS triggers a strong innate immune response, which leads to the production of pro-inflammatory cytokines and activation of immune cells. Over time, the immune system adapts to the presence of LPS and becomes less responsive to subsequent exposures, a phenomenon known as endotoxin tolerance. This tolerance helps protect the host from excessive inflammation and tissue damage during subsequent exposures to LPS. -------------------------------------------------------------------------------- /experiments/trained_immunity/ai_messages/baseline--training_LPS: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: Attenuated innate immune response 3 | explanation: Upon rechallenge with high-dose LPS after 90 days, the mouse is likely to exhibit an attenuated innate immune response. This is due to a phenomenon called endotoxin tolerance, where prior exposure to low-dose LPS can lead to a reduced inflammatory response upon subsequent exposure to higher doses of LPS. This tolerance is a protective mechanism that prevents excessive inflammation and tissue damage. However, the duration of endotoxin tolerance can vary, and it may not be as pronounced after 90 days compared to a shorter time interval. -------------------------------------------------------------------------------- /experiments/trained_immunity/ai_messages/baseline--training_betaglucan: -------------------------------------------------------------------------------- 1 | conclusion: 2 | outcome: Enhanced innate immune response 3 | explanation: Beta-glucan is a known immunomodulator that can prime the immune system, particularly the innate immune response. Upon rechallenge with high-dose LPS after 90 days, the mouse is likely to exhibit an enhanced innate immune response due to the previous exposure to beta-glucan. This response may include increased phagocytosis, production of pro-inflammatory cytokines, and activation of immune cells such as macrophages and neutrophils. -------------------------------------------------------------------------------- /experiments/trained_immunity/prompts/tolerance_LPS: -------------------------------------------------------------------------------- 1 | A mouse is injected with high-dose LPS. What is the innate immune response upon rechallenge with high-dose LPS after 90 days? Focus on novelty. -------------------------------------------------------------------------------- /experiments/trained_immunity/prompts/training_LPS: -------------------------------------------------------------------------------- 1 | A mouse is injected with low-dose LPS. What is the innate immune response upon rechallenge with high-dose LPS after 90 days? Focus on novelty. -------------------------------------------------------------------------------- /experiments/trained_immunity/prompts/training_betaglucan: -------------------------------------------------------------------------------- 1 | A mouse is injected with beta-glucan. What is the innate immune response upon rechallenge with high-dose LPS after 90 days? Focus on novelty. -------------------------------------------------------------------------------- /expert_feedback/annotation.csv: -------------------------------------------------------------------------------- 1 | experiment,prompt 2 | mouse_immunology,Cyanide 3 | mouse_immunology,YUMM_1_7 4 | trained_immunity,tolerance_LPS 5 | trained_immunity,training_LPS 6 | trained_immunity,training_betaglucan 7 | sepsis_treatment,sepsis_hyperinflammation 8 | sepsis_treatment,sepsis_immunoparalysis 9 | glioblastoma_survival,generated_PTEN_EGFR_MGMT_high_methylated\ -------------------------------------------------------------------------------- /expert_feedback/feedback_form.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/OpenBioLink/SimulateGPT/dd1ba1a876888e6c03633b4b6aae9634df02932c/expert_feedback/feedback_form.xlsx -------------------------------------------------------------------------------- /expert_feedback/feedback_form_FINAL_experts.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/OpenBioLink/SimulateGPT/dd1ba1a876888e6c03633b4b6aae9634df02932c/expert_feedback/feedback_form_FINAL_experts.xlsx -------------------------------------------------------------------------------- /expert_feedback/feedback_form_FINAL_groundtruth.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/OpenBioLink/SimulateGPT/dd1ba1a876888e6c03633b4b6aae9634df02932c/expert_feedback/feedback_form_FINAL_groundtruth.xlsx -------------------------------------------------------------------------------- /expert_feedback/likert_scale.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/OpenBioLink/SimulateGPT/dd1ba1a876888e6c03633b4b6aae9634df02932c/expert_feedback/likert_scale.png -------------------------------------------------------------------------------- /expert_feedback/questions.txt: -------------------------------------------------------------------------------- 1 | The output reflects the consensus in the scientific and clinical community. 2 | The output contains evidence of correct recall of knowledge. 3 | The output contains evidence of incorrect recall of knowledge. 4 | The output contains evidence of correct reasoning steps. 5 | The output contains evidence of incorrect reasoning steps. 6 | The output contains content it shouldn’t contain. 7 | The output omits content it shouldn’t omit. 8 | The answer contains (novel) concepts that are not typically known in the scientific and clinical community. 9 | I felt qualified to evaluate this topic. -------------------------------------------------------------------------------- /expert_feedback/results/feedback_form_expert1.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/OpenBioLink/SimulateGPT/dd1ba1a876888e6c03633b4b6aae9634df02932c/expert_feedback/results/feedback_form_expert1.xlsx -------------------------------------------------------------------------------- /expert_feedback/results/feedback_form_expert2.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/OpenBioLink/SimulateGPT/dd1ba1a876888e6c03633b4b6aae9634df02932c/expert_feedback/results/feedback_form_expert2.xlsx -------------------------------------------------------------------------------- /expert_feedback/results/feedback_form_expert3.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/OpenBioLink/SimulateGPT/dd1ba1a876888e6c03633b4b6aae9634df02932c/expert_feedback/results/feedback_form_expert3.xlsx -------------------------------------------------------------------------------- /expert_feedback/results/stats/AGGREGATED.csv: -------------------------------------------------------------------------------- 1 | "name","better_count","same_count","worse_count","chi_square_p","wilcox_rank_p" 2 | "EXP_ALL",19,144,29,4.2537244369516e-38,0.150580418316109 3 | "EXP_glioblastoma_survival",1,17,6,1.17044426610451e-11,0.0726009394499574 4 | "EXP_mouse_immunology",8,40,0,1.6698595501026e-09,0.00596207853209371 5 | "EXP_sepsis_treatment",9,27,12,1.72369821767404e-34,0.525548635108673 6 | "EXP_trained_immunity",1,60,11,3.07639254946243e-11,0.00447837335517295 7 | "OUT_ALL",39,145,8,9.08185407963885e-56,6.30969259220478e-06 8 | "OUT_glioblastoma_survival",1,23,0,0.647775867262407,1 9 | "OUT_mouse_immunology",13,30,5,1.0759600263987e-29,0.0627713915732494 10 | "OUT_sepsis_treatment",20,25,3,2.72771534400018e-67,0.000419711649812927 11 | "OUT_trained_immunity",5,67,0,0.0233123520069742,0.0368884257070499 12 | "SIM_sepsis_treatment",17,29,2,2.50096037802421e-47,0.000630148983156009 13 | -------------------------------------------------------------------------------- /expert_feedback/results/stats/EXP_ALL.csv: -------------------------------------------------------------------------------- 1 | "question","better_count","same_count","worse_count","chi_square_p","wilcox_rank_p" 2 | "The.answer.contains..novel..concepts.that.are.not.typically.known.in.the.scientific.and.clinical.community.",1,22,1,0.75525355295313,1 3 | "The.output.contains.content.it.shouldn.t.contain.",1,19,4,4.17699518025379e-05,0.233037982273905 4 | "The.output.contains.evidence.of.correct.reasoning.steps.",3,16,5,2.94021441477775e-10,0.529650670053343 5 | "The.output.contains.evidence.of.correct.recall.of.knowledge.",5,13,6,3.35228794935666e-19,0.801613520941505 6 | "The.output.contains.evidence.of.incorrect.reasoning.steps.",2,19,3,0.00117087962079118,0.765594483995764 7 | "The.output.contains.evidence.of.incorrect.recall.of.knowledge.",1,21,2,0.159180054655866,0.772829992684448 8 | "The.output.omits.content.it.shouldn.t.omit.",4,16,4,1.55669359652894e-09,1 9 | "The.output.reflects.the.consensus.in.the.scientific.and.clinical.community.",2,18,4,7.718196582182e-06,0.484018354704782 10 | -------------------------------------------------------------------------------- /expert_feedback/results/stats/EXP_glioblastoma_survival.csv: -------------------------------------------------------------------------------- 1 | "question","better_count","same_count","worse_count","chi_square_p","wilcox_rank_p" 2 | "The.answer.contains..novel..concepts.that.are.not.typically.known.in.the.scientific.and.clinical.community.",0,3,0,0.924088559405177,NA 3 | "The.output.contains.content.it.shouldn.t.contain.",0,3,0,0.924088559405177,NA 4 | "The.output.contains.evidence.of.correct.reasoning.steps.",0,2,1,0.00282733402310738,1 5 | "The.output.contains.evidence.of.correct.recall.of.knowledge.",1,1,1,6.09055145265071e-06,1 6 | "The.output.contains.evidence.of.incorrect.reasoning.steps.",0,2,1,0.00282733402310738,1 7 | "The.output.contains.evidence.of.incorrect.recall.of.knowledge.",0,3,0,0.924088559405177,NA 8 | "The.output.omits.content.it.shouldn.t.omit.",0,2,1,0.00282733402310738,1 9 | "The.output.reflects.the.consensus.in.the.scientific.and.clinical.community.",0,1,2,9.86423759612801e-12,0.34577858615116 10 | -------------------------------------------------------------------------------- /expert_feedback/results/stats/EXP_mouse_immunology.csv: -------------------------------------------------------------------------------- 1 | "question","better_count","same_count","worse_count","chi_square_p","wilcox_rank_p" 2 | "The.answer.contains..novel..concepts.that.are.not.typically.known.in.the.scientific.and.clinical.community.",0,6,0,0.853939665623535,NA 3 | "The.output.contains.content.it.shouldn.t.contain.",0,6,0,0.853939665623535,NA 4 | "The.output.contains.evidence.of.correct.reasoning.steps.",2,4,0,7.99381767822057e-06,0.34577858615116 5 | "The.output.contains.evidence.of.correct.recall.of.knowledge.",2,4,0,7.99381767822057e-06,0.34577858615116 6 | "The.output.contains.evidence.of.incorrect.reasoning.steps.",1,5,0,0.0799530421736451,1 7 | "The.output.contains.evidence.of.incorrect.recall.of.knowledge.",0,6,0,0.853939665623535,NA 8 | "The.output.omits.content.it.shouldn.t.omit.",3,3,0,8.53463444068325e-13,0.148914673178766 9 | "The.output.reflects.the.consensus.in.the.scientific.and.clinical.community.",0,6,0,0.853939665623535,NA 10 | -------------------------------------------------------------------------------- /expert_feedback/results/stats/EXP_sepsis_treatment.csv: -------------------------------------------------------------------------------- 1 | "question","better_count","same_count","worse_count","chi_square_p","wilcox_rank_p" 2 | "The.answer.contains..novel..concepts.that.are.not.typically.known.in.the.scientific.and.clinical.community.",0,5,1,0.0799530421736451,1 3 | "The.output.contains.content.it.shouldn.t.contain.",1,2,3,4.72081179653875e-14,0.423710797166793 4 | "The.output.contains.evidence.of.correct.reasoning.steps.",1,4,1,0.00628131805850577,1 5 | "The.output.contains.evidence.of.correct.recall.of.knowledge.",2,2,2,3.70948169973856e-11,1 6 | "The.output.contains.evidence.of.incorrect.reasoning.steps.",1,4,1,0.00628131805850577,1 7 | "The.output.contains.evidence.of.incorrect.recall.of.knowledge.",1,4,1,0.00628131805850577,1 8 | "The.output.omits.content.it.shouldn.t.omit.",1,3,2,5.26960443561801e-07,0.772829992684448 9 | "The.output.reflects.the.consensus.in.the.scientific.and.clinical.community.",2,3,1,5.26960443561801e-07,0.772829992684448 10 | -------------------------------------------------------------------------------- /expert_feedback/results/stats/EXP_trained_immunity.csv: -------------------------------------------------------------------------------- 1 | "question","better_count","same_count","worse_count","chi_square_p","wilcox_rank_p" 2 | "The.answer.contains..novel..concepts.that.are.not.typically.known.in.the.scientific.and.clinical.community.",1,8,0,0.231098262365644,1 3 | "The.output.contains.content.it.shouldn.t.contain.",0,8,1,0.231098262365644,1 4 | "The.output.contains.evidence.of.correct.reasoning.steps.",0,6,3,2.26011926961504e-08,0.148914673178766 5 | "The.output.contains.evidence.of.correct.recall.of.knowledge.",0,6,3,2.26011926961504e-08,0.148914673178766 6 | "The.output.contains.evidence.of.incorrect.reasoning.steps.",0,8,1,0.231098262365644,1 7 | "The.output.contains.evidence.of.incorrect.recall.of.knowledge.",0,8,1,0.231098262365644,1 8 | "The.output.omits.content.it.shouldn.t.omit.",0,8,1,0.231098262365644,1 9 | "The.output.reflects.the.consensus.in.the.scientific.and.clinical.community.",0,8,1,0.231098262365644,1 10 | -------------------------------------------------------------------------------- /expert_feedback/results/stats/OUT_ALL.csv: -------------------------------------------------------------------------------- 1 | "question","better_count","same_count","worse_count","chi_square_p","wilcox_rank_p" 2 | "The.answer.contains..novel..concepts.that.are.not.typically.known.in.the.scientific.and.clinical.community.",0,24,0,0.531751530130571,NA 3 | "The.output.contains.content.it.shouldn.t.contain.",1,23,0,0.647775867262407,1 4 | "The.output.contains.evidence.of.correct.reasoning.steps.",9,14,1,4.65628799666873e-27,0.0134236978545277 5 | "The.output.contains.evidence.of.correct.recall.of.knowledge.",11,12,1,4.86189076839017e-41,0.00447837335517295 6 | "The.output.contains.evidence.of.incorrect.reasoning.steps.",4,19,1,4.17699518025379e-05,0.233037982273905 7 | "The.output.contains.evidence.of.incorrect.recall.of.knowledge.",3,20,1,0.00606474272022038,0.423710797166793 8 | "The.output.omits.content.it.shouldn.t.omit.",6,17,1,1.17044426610451e-11,0.0726009394499574 9 | "The.output.reflects.the.consensus.in.the.scientific.and.clinical.community.",5,16,3,2.94021441477775e-10,0.529650670053343 10 | -------------------------------------------------------------------------------- /expert_feedback/results/stats/OUT_glioblastoma_survival.csv: -------------------------------------------------------------------------------- 1 | "question","better_count","same_count","worse_count","chi_square_p","wilcox_rank_p" 2 | "The.answer.contains..novel..concepts.that.are.not.typically.known.in.the.scientific.and.clinical.community.",0,3,0,0.924088559405177,NA 3 | "The.output.contains.content.it.shouldn.t.contain.",0,3,0,0.924088559405177,NA 4 | "The.output.contains.evidence.of.correct.reasoning.steps.",0,3,0,0.924088559405177,NA 5 | "The.output.contains.evidence.of.correct.recall.of.knowledge.",1,2,0,0.00282733402310738,1 6 | "The.output.contains.evidence.of.incorrect.reasoning.steps.",0,3,0,0.924088559405177,NA 7 | "The.output.contains.evidence.of.incorrect.recall.of.knowledge.",0,3,0,0.924088559405177,NA 8 | "The.output.omits.content.it.shouldn.t.omit.",0,3,0,0.924088559405177,NA 9 | "The.output.reflects.the.consensus.in.the.scientific.and.clinical.community.",0,3,0,0.924088559405177,NA 10 | -------------------------------------------------------------------------------- /expert_feedback/results/stats/OUT_mouse_immunology.csv: -------------------------------------------------------------------------------- 1 | "question","better_count","same_count","worse_count","chi_square_p","wilcox_rank_p" 2 | "The.answer.contains..novel..concepts.that.are.not.typically.known.in.the.scientific.and.clinical.community.",0,6,0,0.853939665623535,NA 3 | "The.output.contains.content.it.shouldn.t.contain.",0,6,0,0.853939665623535,NA 4 | "The.output.contains.evidence.of.correct.reasoning.steps.",5,0,1,4.6134768683222e-37,0.129430674437906 5 | "The.output.contains.evidence.of.correct.recall.of.knowledge.",3,3,0,8.53463444068325e-13,0.148914673178766 6 | "The.output.contains.evidence.of.incorrect.reasoning.steps.",1,4,1,0.00628131805850577,1 7 | "The.output.contains.evidence.of.incorrect.recall.of.knowledge.",1,5,0,0.0799530421736451,1 8 | "The.output.omits.content.it.shouldn.t.omit.",2,3,1,5.26960443561801e-07,0.772829992684448 9 | "The.output.reflects.the.consensus.in.the.scientific.and.clinical.community.",1,3,2,5.26960443561801e-07,0.772829992684448 10 | -------------------------------------------------------------------------------- /expert_feedback/results/stats/OUT_sepsis_treatment.csv: -------------------------------------------------------------------------------- 1 | "question","better_count","same_count","worse_count","chi_square_p","wilcox_rank_p" 2 | "The.answer.contains..novel..concepts.that.are.not.typically.known.in.the.scientific.and.clinical.community.",0,6,0,0.853939665623535,NA 3 | "The.output.contains.content.it.shouldn.t.contain.",1,5,0,0.0799530421736451,1 4 | "The.output.contains.evidence.of.correct.reasoning.steps.",3,3,0,8.53463444068325e-13,0.148914673178766 5 | "The.output.contains.evidence.of.correct.recall.of.knowledge.",4,1,1,4.51613376886997e-24,0.233037982273905 6 | "The.output.contains.evidence.of.incorrect.reasoning.steps.",2,4,0,7.99381767822057e-06,0.34577858615116 7 | "The.output.contains.evidence.of.incorrect.recall.of.knowledge.",2,3,1,5.26960443561801e-07,0.772829992684448 8 | "The.output.omits.content.it.shouldn.t.omit.",4,2,0,9.73031833528653e-23,0.0718606382258516 9 | "The.output.reflects.the.consensus.in.the.scientific.and.clinical.community.",4,1,1,4.51613376886997e-24,0.233037982273905 10 | -------------------------------------------------------------------------------- /expert_feedback/results/stats/OUT_trained_immunity.csv: -------------------------------------------------------------------------------- 1 | "question","better_count","same_count","worse_count","chi_square_p","wilcox_rank_p" 2 | "The.answer.contains..novel..concepts.that.are.not.typically.known.in.the.scientific.and.clinical.community.",0,9,0,0.789115875424991,NA 3 | "The.output.contains.content.it.shouldn.t.contain.",0,9,0,0.789115875424991,NA 4 | "The.output.contains.evidence.of.correct.reasoning.steps.",1,8,0,0.231098262365644,1 5 | "The.output.contains.evidence.of.correct.recall.of.knowledge.",3,6,0,2.26011926961504e-08,0.148914673178766 6 | "The.output.contains.evidence.of.incorrect.reasoning.steps.",1,8,0,0.231098262365644,1 7 | "The.output.contains.evidence.of.incorrect.recall.of.knowledge.",0,9,0,0.789115875424991,NA 8 | "The.output.omits.content.it.shouldn.t.omit.",0,9,0,0.789115875424991,NA 9 | "The.output.reflects.the.consensus.in.the.scientific.and.clinical.community.",0,9,0,0.789115875424991,NA 10 | -------------------------------------------------------------------------------- /expert_feedback/results/stats/SIM_sepsis_treatment.csv: -------------------------------------------------------------------------------- 1 | "question","better_count","same_count","worse_count","chi_square_p","wilcox_rank_p" 2 | "The.answer.contains..novel..concepts.that.are.not.typically.known.in.the.scientific.and.clinical.community.",0,6,0,0.853939665623535,NA 3 | "The.output.contains.content.it.shouldn.t.contain.",3,1,2,2.78843091706882e-18,0.765594483995764 4 | "The.output.contains.evidence.of.correct.reasoning.steps.",3,3,0,8.53463444068325e-13,0.148914673178766 5 | "The.output.contains.evidence.of.correct.recall.of.knowledge.",2,4,0,7.99381767822057e-06,0.34577858615116 6 | "The.output.contains.evidence.of.incorrect.reasoning.steps.",3,3,0,8.53463444068325e-13,0.148914673178766 7 | "The.output.contains.evidence.of.incorrect.recall.of.knowledge.",2,4,0,7.99381767822057e-06,0.34577858615116 8 | "The.output.omits.content.it.shouldn.t.omit.",2,4,0,7.99381767822057e-06,0.34577858615116 9 | "The.output.reflects.the.consensus.in.the.scientific.and.clinical.community.",2,4,0,7.99381767822057e-06,0.34577858615116 10 | -------------------------------------------------------------------------------- /game_of_life/human_prompt: -------------------------------------------------------------------------------- 1 | Generation 0: 2 | ``` 3 | |(0,0):dead|(0,1):live|(0,2):dead|(0,3):dead| 4 | |(1,0):dead|(1,1):dead|(1,2):live|(1,3):dead| 5 | |(2,0):live|(2,1):live|(2,2):live|(2,3):dead| 6 | |(3,0):dead|(3,1):dead|(3,2):dead|(3,3):dead| 7 | ``` 8 | Simulate 4 generations -------------------------------------------------------------------------------- /game_of_life/latex_output: -------------------------------------------------------------------------------- 1 | Generation 0: 2 | \begin{bmatrix} 3 | & \blacksquare & & \\ 4 | & & \blacksquare & \\ 5 | \blacksquare & \blacksquare & \blacksquare & \\ 6 | & & & \\ 7 | \end{bmatrix} 8 | 9 | Generation 1: 10 | \begin{bmatrix} 11 | & & & \\ 12 | \blacksquare & & \blacksquare & \\ 13 | & \blacksquare & \blacksquare & \\ 14 | & \blacksquare & & \\ 15 | \end{bmatrix} 16 | 17 | Generation 2: 18 | \begin{bmatrix} 19 | & & & \\ 20 | & & \blacksquare & \\ 21 | \blacksquare & & \blacksquare & \\ 22 | & \blacksquare & \blacksquare & \\ 23 | \end{bmatrix} 24 | 25 | Generation 3: 26 | \begin{bmatrix} 27 | & & & \\ 28 | & \blacksquare & & \\ 29 | & & \blacksquare & \blacksquare \\ 30 | & \blacksquare & \blacksquare & \\ 31 | \end{bmatrix} 32 | 33 | Generation 4: 34 | \begin{bmatrix} 35 | & & & \\ 36 | & & \blacksquare & \\ 37 | & & & \blacksquare \\ 38 | & \blacksquare & \blacksquare & \blacksquare \\ 39 | \end{bmatrix} -------------------------------------------------------------------------------- /game_of_life/main.md: -------------------------------------------------------------------------------- 1 | # Conway's Game of life 2 | 3 | Objective: Show that LLMs/GPT4 is able to perform step-wise simulations 4 | 5 | [system_prompt](system_prompt) (in the current version, with latex instructions) was run with [human_prompt](human_prompt). This led to the correct simulation. 6 | 7 | See file [ai_message](ai_message) for the final result. Timeouts were indicated with 8 | 9 | [latex_output](latex_output) contains the latex outputs manually copied from the ai_message. 10 | 11 | A python-based simulation of game of life (written by GPT4 as well) can be found in [game_of_life_simulator.py](game_of_life_simulator.py) 12 | -------------------------------------------------------------------------------- /game_of_life/python_output: -------------------------------------------------------------------------------- 1 | Initial grid: 2 | 0100 3 | 0010 4 | 1110 5 | 0000 6 | Generation 1 7 | ``` 8 | 0000 9 | 1010 10 | 0110 11 | 0100 12 | ``` 13 | 14 | Generation 2 15 | ``` 16 | 0000 17 | 0010 18 | 1010 19 | 0110 20 | ``` 21 | 22 | Generation 3 23 | ``` 24 | 0000 25 | 0100 26 | 0011 27 | 0110 28 | ``` 29 | 30 | Generation 4 31 | ``` 32 | 0000 33 | 0010 34 | 0001 35 | 0111 36 | ``` 37 | 38 | -------------------------------------------------------------------------------- /reference_analysis/all_manual_check.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/OpenBioLink/SimulateGPT/dd1ba1a876888e6c03633b4b6aae9634df02932c/reference_analysis/all_manual_check.xlsx -------------------------------------------------------------------------------- /reference_analysis/cross_ref/crc--baseline--crc_apc_impact_2020_0.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/crc--baseline--crc_apc_impact_2020_1.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/crc--baseline--crc_apc_impact_2020_10.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/crc--baseline--crc_apc_impact_2020_11.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/crc--baseline--crc_apc_impact_2020_12.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/crc--baseline--crc_apc_impact_2020_13.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/crc--baseline--crc_apc_impact_2020_14.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/crc--baseline--crc_apc_impact_2020_15.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/crc--baseline--crc_apc_impact_2020_16.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/crc--baseline--crc_apc_impact_2020_17.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/crc--baseline--crc_apc_impact_2020_18.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/crc--baseline--crc_apc_impact_2020_19.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/crc--baseline--crc_apc_impact_2020_20.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/crc--baseline--crc_apc_impact_2020_3.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/crc--baseline--crc_apc_impact_2020_5.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/crc--baseline--crc_apc_impact_2020_6.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/crc--baseline--crc_apc_impact_2020_8.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/crc--baseline--crc_apc_impact_2020_9.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/crc--high_complexity--crc_apc_impact_2020_14.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | 0,experiments/crc/ai_messages/high_complexity--crc_apc_impact_2020_14,[1],"Siegel RL, Miller KD, Jemal A. 2020. CA Cancer J Clin. Cancer statistics, 2020.",10.3322/caac.21590,https://doi.org/10.3322/caac.21590 3 | 1,experiments/crc/ai_messages/high_complexity--crc_apc_impact_2020_14,[2],"Van Cutsem E, Cervantes A, Nordlinger B, Arnold D. 2014. Ann Oncol. Metastatic colorectal cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up.",10.1093/annonc/mdu260,https://doi.org/10.1093/annonc/mdu260 4 | 2,experiments/crc/ai_messages/high_complexity--crc_apc_impact_2020_14,[3],"Hanahan D, Weinberg RA. 2011. Cell. Hallmarks of cancer: the next generation.",10.1016/j.cell.2011.02.013,https://doi.org/10.1016/j.cell.2011.02.013 5 | 3,experiments/crc/ai_messages/high_complexity--crc_apc_impact_2020_14,[4],"Mehlen P, Puisieux A. 2006. Nat Rev Cancer. Metastasis: a question of life or death.",10.1038/nrc1886,https://doi.org/10.1038/nrc1886 6 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--baseline--AGAP5.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--baseline--ANKLE2.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--baseline--AQP8.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--baseline--CCT5.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--baseline--DCTN3.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--baseline--DHX16.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--baseline--DSPP.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--baseline--EIF2B3.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--baseline--FAM71C.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--baseline--GK2.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--baseline--GPR78.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--baseline--GTF3C1.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--baseline--H2BC1.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--baseline--HAUS4.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--baseline--HNRNPK.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--baseline--HNRNPL.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--baseline--HSPD1.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--baseline--IFNA10.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--baseline--IL12B.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--baseline--IL17F.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--baseline--KIF2B.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--baseline--KRT25.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--baseline--KRT26.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--baseline--KRT82.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--baseline--KRTAP9-3.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--baseline--LGALS14.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--baseline--MRGPRX2.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--baseline--NDST4.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--baseline--NDUFB3.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--baseline--NOC3L.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--baseline--OPA1.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--baseline--OR3A3.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--baseline--OR52W1.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--baseline--OR5T2.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--baseline--POLR3H.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--baseline--PRPF19.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--baseline--PSMA7.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--baseline--PSMB7.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--baseline--RBX1.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--baseline--RPL27.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--baseline--RPL37A.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--baseline--RPL7A.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--baseline--S100A7A.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--baseline--SDR9C7.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--baseline--SLC25A2.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--baseline--SS18L2.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--baseline--SUPT16H.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--baseline--TIMM29.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--baseline--TMPRSS11F.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--baseline--ZP2.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--high_complexity--AGAP5.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | 0,experiments/gene_essentiality_cancer/ai_messages/high_complexity--AGAP5,[1],"Nie, Z., & Randazzo, P. A. (2006). Arf GAPs and membrane traffic. Journal of Cell Science, 119(Pt 7), 1203-1211.",10.1242/jcs.02924,https://doi.org/10.1242/jcs.02924 3 | 1,experiments/gene_essentiality_cancer/ai_messages/high_complexity--AGAP5,[2],"Hart, T., Chandrashekhar, M., Aregger, M., Steinhart, Z., Brown, K. R., MacLeod, G., ... & Moffat, J. (2015). High-resolution CRISPR screens reveal fitness genes and genotype-specific cancer liabilities. Cell, 163(6), 1515-1526.",10.1016/j.cell.2015.11.015,https://doi.org/10.1016/j.cell.2015.11.015 4 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--high_complexity--ANKLE2.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | 0,experiments/gene_essentiality_cancer/ai_messages/high_complexity--ANKLE2,[1],"Brachner A, Foisner R. 2011. Nucleus. ANKLE2, a new nuclear envelope protein involved in human primary microcephaly.",10.1042/bst20110724,https://doi.org/10.1042/bst20110724 3 | 1,experiments/gene_essentiality_cancer/ai_messages/high_complexity--ANKLE2,[2],"Hussain MS, Baig SM, Neumann S, et al. 2013. Am J Hum Genet. A truncating mutation of CEP135 causes primary microcephaly and disturbed centrosomal function.",10.1016/j.ajhg.2012.03.016,https://doi.org/10.1016/j.ajhg.2012.03.016 4 | 2,experiments/gene_essentiality_cancer/ai_messages/high_complexity--ANKLE2,[3],"De Vos WH, Houben F, Kamps M, et al. 2011. Biochim Biophys Acta. Repetitive disruptions of the nuclear envelope invoke temporary loss of cellular compartmentalization in laminopathies.",10.1093/hmg/ddr344,https://doi.org/10.1093/hmg/ddr344 5 | 3,experiments/gene_essentiality_cancer/ai_messages/high_complexity--ANKLE2,[4],"Shay JW, Wright WE. 2011. Nat Rev Drug Discov. Telomerase therapeutics for cancer: challenges and new directions.",10.1038/nrd2081,https://doi.org/10.1038/nrd2081 6 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--high_complexity--AQP8.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | 0,experiments/gene_essentiality_cancer/ai_messages/high_complexity--AQP8,[1],"Agre P, Kozono D. 2003. Nature Reviews Molecular Cell Biology. Aquaporin water channels: molecular mechanisms for human diseases.",10.1016/s0014-5793(03)01083-4,https://doi.org/10.1016/s0014-5793(03)01083-4 3 | 1,experiments/gene_essentiality_cancer/ai_messages/high_complexity--AQP8,[2],"Ishibashi K, Hara S, Kondo S. 2009. Pflugers Archiv: European Journal of Physiology. Aquaporin water channels in mammals.",10.1007/s10157-008-0118-6,https://doi.org/10.1007/s10157-008-0118-6 4 | 2,experiments/gene_essentiality_cancer/ai_messages/high_complexity--AQP8,[3],"Papadopoulos MC, Saadoun S. 2015. British Journal of Cancer. Key roles of aquaporins in tumor biology.",10.1016/j.bbamem.2014.09.001,https://doi.org/10.1016/j.bbamem.2014.09.001 5 | 3,experiments/gene_essentiality_cancer/ai_messages/high_complexity--AQP8,[4],"Wang J, Feng L, Zhu Z, Zheng M, Wang D, Chen Z, Sun H. 2015. Oncotarget. Aquaporin 8 mediates human esophageal cancer Eca-109 cell migration via the EGFR-Erk1/2 pathway.",10.18632/oncotarget.8576,https://doi.org/10.18632/oncotarget.8576 6 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--high_complexity--DCTN3.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | 0,experiments/gene_essentiality_cancer/ai_messages/high_complexity--DCTN3,[1],"Schroer, T.A. (2004) Traffic. Dynactin.",10.1016/b0-12-443710-9/00187-3,https://doi.org/10.1016/b0-12-443710-9/00187-3 3 | 1,experiments/gene_essentiality_cancer/ai_messages/high_complexity--DCTN3,[2],"Zhang, Y. et al. (2015) Oncotarget. Overexpression of DCTN3 promotes cell proliferation in breast cancer.",10.18632/oncotarget.8119,https://doi.org/10.18632/oncotarget.8119 4 | 2,experiments/gene_essentiality_cancer/ai_messages/high_complexity--DCTN3,[3],"Winzeler, E.A. et al. (1999) Science. Functional Characterization of the S. cerevisiae Genome by Gene Deletion and Parallel Analysis.",10.1002/047084664x.ch14,https://doi.org/10.1002/047084664x.ch14 5 | 3,experiments/gene_essentiality_cancer/ai_messages/high_complexity--DCTN3,[4],"Hart, T. et al. (2015) Cell. High-Resolution CRISPR Screens Reveal Fitness Genes and Genotype-Specific Cancer Liabilities.",10.3410/f.725961145.793520856,https://doi.org/10.3410/f.725961145.793520856 6 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--high_complexity--DHX16.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | 0,experiments/gene_essentiality_cancer/ai_messages/high_complexity--DHX16,[1],"Gorbalenya AE, Koonin EV. 1993. Nucleic Acids Research. Helicases: amino acid sequence comparisons and structure-function relationships.",10.1016/s0959-440x(05)80116-2,https://doi.org/10.1016/s0959-440x(05)80116-2 3 | 1,experiments/gene_essentiality_cancer/ai_messages/high_complexity--DHX16,[2],Fuller-Pace FV. 2013. Nature Reviews Cancer. DEAD box RNA helicase functions in cancer.,10.4161/rna.23312,https://doi.org/10.4161/rna.23312 4 | 2,experiments/gene_essentiality_cancer/ai_messages/high_complexity--DHX16,[3],"Hart T, Moffat J. 2016. Nature Reviews Genetics. BAGEL: a computational framework for identifying essential genes from pooled library screens.",10.1186/s12859-016-1015-8,https://doi.org/10.1186/s12859-016-1015-8 5 | 3,experiments/gene_essentiality_cancer/ai_messages/high_complexity--DHX16,[4],"Blomen VA, Májek P, Jae LT, Bigenzahn JW, Nieuwenhuis J, Staring J, Sacco R, van Diemen FR, Olk N, Stukalov A, Marceau C, Janssen H, Carette JE, Bennett KL, Colinge J, Superti-Furga G, Brummelkamp TR. 2015. Science. Gene essentiality and synthetic lethality in haploid human cells.",10.1126/science.aac7557,https://doi.org/10.1126/science.aac7557 6 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--high_complexity--DSPP.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | 0,experiments/gene_essentiality_cancer/ai_messages/high_complexity--DSPP,[1],"Yamakoshi Y, Hu JC, Fukae M, Zhang H, Simmer JP. 2005. Journal of Biological Chemistry. Dentin Sialophosphoprotein is Processed by MMP-2 and MMP-20 in vitro and in vivo.",10.1074/jbc.m607767200,https://doi.org/10.1074/jbc.m607767200 3 | 1,experiments/gene_essentiality_cancer/ai_messages/high_complexity--DSPP,[2],"Ogbureke KU, Fisher LW. 2004. Journal of Histochemistry & Cytochemistry. Expression of SIBLINGs and their partner MMPs in salivary glands.",10.1177/154405910408300902,https://doi.org/10.1177/154405910408300902 4 | 2,experiments/gene_essentiality_cancer/ai_messages/high_complexity--DSPP,[3],Masters JR. 2000. British Journal of Cancer. Human cancer cell lines: fact and fantasy.,10.1038/35043102,https://doi.org/10.1038/35043102 5 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--high_complexity--FAM71C.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | 0,experiments/gene_essentiality_cancer/ai_messages/high_complexity--FAM71C,[1],NCBI Gene. FAM71C family with sequence similarity 71 member C [Homo sapiens (human)]. https://www.ncbi.nlm.nih.gov/gene/283871,10.1007/978-3-319-67199-4_102580,https://doi.org/10.1007/978-3-319-67199-4_102580 3 | 1,experiments/gene_essentiality_cancer/ai_messages/high_complexity--FAM71C,[2],"Meyers, R. M., Bryan, J. G., McFarland, J. M., Weir, B. A., Sizemore, A. E., Xu, H., ... & Tsherniak, A. (2017). Computational correction of copy number effect improves specificity of CRISPR-Cas9 essentiality screens in cancer cells. Nature genetics, 49(12), 1779-1784.",10.1038/ng.3984,https://doi.org/10.1038/ng.3984 4 | 2,experiments/gene_essentiality_cancer/ai_messages/high_complexity--FAM71C,[3],"Hart, T., Chandrashekhar, M., Aregger, M., Steinhart, Z., Brown, K. R., MacLeod, G., ... & Moffat, J. (2015). High-resolution CRISPR screens reveal fitness genes and genotype-specific cancer liabilities. Cell, 163(6), 1515-1526.",10.1016/j.cell.2015.11.015,https://doi.org/10.1016/j.cell.2015.11.015 5 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--high_complexity--GK2.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | 0,experiments/gene_essentiality_cancer/ai_messages/high_complexity--GK2,[1],"Bairoch A, Apweiler R. 2000. The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000. Nucleic Acids Res. 28(1):45-48.",10.1093/nar/28.1.45,https://doi.org/10.1093/nar/28.1.45 3 | 1,experiments/gene_essentiality_cancer/ai_messages/high_complexity--GK2,[2],"Nelson DL, Cox MM. 2017. Lehninger Principles of Biochemistry. 7th ed. W.H. Freeman and Company.",10.1002/bmb.2005.494033010419,https://doi.org/10.1002/bmb.2005.494033010419 4 | 2,experiments/gene_essentiality_cancer/ai_messages/high_complexity--GK2,[3],"Hanahan D, Weinberg RA. 2011. Hallmarks of cancer: the next generation. Cell. 144(5):646-674.",10.1016/j.cell.2011.02.013,https://doi.org/10.1016/j.cell.2011.02.013 5 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--high_complexity--GPR78.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | 0,experiments/gene_essentiality_cancer/ai_messages/high_complexity--GPR78,[1],"Fredriksson R, Lagerström MC, Lundin LG, Schiöth HB. 2003. The G-protein-coupled receptors in the human genome form five main families. Phylogenetic analysis, paralogon groups, and fingerprints. Molecular Pharmacology 63(6):1256-1272.",10.1124/mol.63.6.1256,https://doi.org/10.1124/mol.63.6.1256 3 | 1,experiments/gene_essentiality_cancer/ai_messages/high_complexity--GPR78,[2],"Hart T, Chandrashekhar M, Aregger M, Steinhart Z, Brown KR, MacLeod G, Mis M, Zimmermann M, Fradet-Turcotte A, Sun S, Mero P, Dirks P, Sidhu S, Roth FP, Rissland OS, Durocher D, Angers S, Moffat J. 2015. High-Resolution CRISPR Screens Reveal Fitness Genes and Genotype-Specific Cancer Liabilities. Cell 163(6):1515-1526.",10.1016/j.cell.2015.11.015,https://doi.org/10.1016/j.cell.2015.11.015 4 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--high_complexity--GTF3C1.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | 0,experiments/gene_essentiality_cancer/ai_messages/high_complexity--GTF3C1,[1],"Lefebvre O, et al. 1992. Cell. Human TFIID Binds to Promoter TATA Boxes Via a Highly Conserved Subunit.",10.1111/j.1742-4658.2006.05468.x,https://doi.org/10.1111/j.1742-4658.2006.05468.x 3 | 1,experiments/gene_essentiality_cancer/ai_messages/high_complexity--GTF3C1,[2],"Hsieh YJ, et al. 1999. Genes Dev. Structure and function of the BTB domain in the Drosophila GAGA factor.",10.1007/s004270050202,https://doi.org/10.1007/s004270050202 4 | 2,experiments/gene_essentiality_cancer/ai_messages/high_complexity--GTF3C1,[3],"DeBerardinis RJ, et al. 2008. Cell Metab. The biology of cancer: metabolic reprogramming fuels cell growth and proliferation.",10.1016/j.cmet.2007.10.002,https://doi.org/10.1016/j.cmet.2007.10.002 5 | 3,experiments/gene_essentiality_cancer/ai_messages/high_complexity--GTF3C1,[4],"Pavon-Eternod M, et al. 2013. Oncogene. tRNA over-expression in breast cancer and functional consequences.",10.1093/nar/gkp787,https://doi.org/10.1093/nar/gkp787 6 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--high_complexity--IL12B.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | 0,experiments/gene_essentiality_cancer/ai_messages/high_complexity--IL12B,[1],Trinchieri G. 2003. Nature Reviews Immunology. Interleukin-12 and the regulation of innate resistance and adaptive immunity.,10.1038/nri1001,https://doi.org/10.1038/nri1001 3 | 1,experiments/gene_essentiality_cancer/ai_messages/high_complexity--IL12B,[2],"Vignali DA, Kuchroo VK. 2012. Nature Immunology. IL-12 family cytokines: immunological playmakers.",10.1038/ni.2366,https://doi.org/10.1038/ni.2366 4 | 2,experiments/gene_essentiality_cancer/ai_messages/high_complexity--IL12B,[3],"Colombo MP, Trinchieri G. 2002. Nature Reviews Cancer. Interleukin-12 in anti-tumor immunity and immunotherapy.",10.1016/s1359-6101(01)00032-6,https://doi.org/10.1016/s1359-6101(01)00032-6 5 | 3,experiments/gene_essentiality_cancer/ai_messages/high_complexity--IL12B,[4],"Teng MW, et al. 2015. Nature Reviews Drug Discovery. Targeting IL-12 and IL-23 cytokines: from bench to bedside.",10.1016/j.ddstr.2006.08.004,https://doi.org/10.1016/j.ddstr.2006.08.004 6 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--high_complexity--IL17F.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | 0,experiments/gene_essentiality_cancer/ai_messages/high_complexity--IL17F,[1],"Gaffen SL, Jain R, Garg AV, Cua DJ. 2014. The IL-23-IL-17 immune axis: from mechanisms to therapeutic testing. Nature Reviews Immunology.",10.1038/nri3707,https://doi.org/10.1038/nri3707 3 | 1,experiments/gene_essentiality_cancer/ai_messages/high_complexity--IL17F,[2],"Iwakura Y, Ishigame H, Saijo S, Nakae S. 2011. Functional specialization of interleukin-17 family members. Immunity.",10.1016/j.immuni.2011.02.012,https://doi.org/10.1016/j.immuni.2011.02.012 4 | 2,experiments/gene_essentiality_cancer/ai_messages/high_complexity--IL17F,[3],"Wu X, Yang T, Liu X, Guo JN, Xie T, Ding Y, Lin M, Yang H. 2013. IL-17 promotes tumor angiogenesis through Stat3 pathway mediated upregulation of VEGF in gastric cancer. Tumour Biology.",10.1007/s13277-015-4372-4,https://doi.org/10.1007/s13277-015-4372-4 5 | 3,experiments/gene_essentiality_cancer/ai_messages/high_complexity--IL17F,[4],"Benchetrit F, Ciree A, Vives V, Warnier G, Gey A, Sautès-Fridman C, Fossiez F, Haicheur N, Fridman WH, Tartour E. 2002. Interleukin-17 inhibits tumor cell growth by means of a T-cell-dependent mechanism. Blood.",10.1182/blood.v99.6.2114,https://doi.org/10.1182/blood.v99.6.2114 6 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--high_complexity--KRT25.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | 0,experiments/gene_essentiality_cancer/ai_messages/high_complexity--KRT25,[1],"Schweizer J, Bowden PE, Coulombe PA, et al. 2006. J Cell Biol. New consensus nomenclature for mammalian keratins.",10.1083/jcb.200603161,https://doi.org/10.1083/jcb.200603161 3 | 1,experiments/gene_essentiality_cancer/ai_messages/high_complexity--KRT25,[2],"Moll R, Divo M, Langbein L. 2008. Histochem Cell Biol. The human keratins: biology and pathology.",10.1007/s00418-008-0435-6,https://doi.org/10.1007/s00418-008-0435-6 4 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--high_complexity--KRT26.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | 0,experiments/gene_essentiality_cancer/ai_messages/high_complexity--KRT26,[1],"Moll R, Divo M, Langbein L. 2008. The human keratins: biology and pathology. Histochemistry and Cell Biology.",10.1007/s00418-008-0435-6,https://doi.org/10.1007/s00418-008-0435-6 3 | 1,experiments/gene_essentiality_cancer/ai_messages/high_complexity--KRT26,[2],"Shimomura Y, Wajid M, Petukhova L, Kurban M, Christiano AM. 2010. Autosomal-dominant woolly hair resulting from disruption of keratin 74 (KRT74), a potential determinant of human hair texture. American Journal of Human Genetics.",10.1016/j.ajhg.2010.02.025,https://doi.org/10.1016/j.ajhg.2010.02.025 4 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--high_complexity--KRT82.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | 0,experiments/gene_essentiality_cancer/ai_messages/high_complexity--KRT82,[1],"Schweizer J, Bowden PE, Coulombe PA, et al. 2006. J Cell Biol. New consensus nomenclature for mammalian keratins.",10.1083/jcb.200603161,https://doi.org/10.1083/jcb.200603161 3 | 1,experiments/gene_essentiality_cancer/ai_messages/high_complexity--KRT82,[2],"Coulombe PA, Omary MB. 2002. Nat Rev Mol Cell Biol. 'Hard' and 'soft' principles defining the structure, function and regulation of keratin intermediate filaments.",10.1016/s0955-0674(01)00301-5,https://doi.org/10.1016/s0955-0674(01)00301-5 4 | 2,experiments/gene_essentiality_cancer/ai_messages/high_complexity--KRT82,[3],"Langbein L, Rogers MA, Winter H, et al. 2001. J Biol Chem. The catalog of human hair keratins. I. Expression of the nine type I members in the hair follicle.",10.1074/jbc.m103305200,https://doi.org/10.1074/jbc.m103305200 5 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--high_complexity--KRTAP9-3.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | 0,experiments/gene_essentiality_cancer/ai_messages/high_complexity--KRTAP9-3,[1],"Shimomura Y, Wajid M, Petukhova L, Kurban M, Christiano AM. 2010. Human Molecular Genetics. Mutations in the keratin-associated protein 9-3 (KRTAP9-3) gene underlie autosomal dominant woolly hair.",10.1016/j.ajhg.2010.02.025,https://doi.org/10.1016/j.ajhg.2010.02.025 3 | 1,experiments/gene_essentiality_cancer/ai_messages/high_complexity--KRTAP9-3,[2],"Rogers MA, Langbein L, Praetzel-Wunder S, Winter H, Schweizer J. 2006. Journal of Investigative Dermatology. Human hair keratin-associated proteins (KAPs).",10.1016/s0074-7696(06)51006-x,https://doi.org/10.1016/s0074-7696(06)51006-x 4 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--high_complexity--LGALS14.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | 0,experiments/gene_essentiality_cancer/ai_messages/high_complexity--LGALS14,[1],"Liu FT, Rabinovich GA. 2005. Annual Review of Immunology. Galectins: Regulators of acute and chronic inflammation.",10.1111/j.1749-6632.2009.05131.x,https://doi.org/10.1111/j.1749-6632.2009.05131.x 3 | 1,experiments/gene_essentiality_cancer/ai_messages/high_complexity--LGALS14,[2],"Rabinovich GA, Croci DO. 2012. Nature Reviews Immunology. Regulatory circuits mediated by lectin-glycan interactions in autoimmunity and cancer.",10.1016/j.immuni.2012.03.004,https://doi.org/10.1016/j.immuni.2012.03.004 4 | 2,experiments/gene_essentiality_cancer/ai_messages/high_complexity--LGALS14,[3],"Hart T, Moffat J. 2016. Nature Reviews Genetics. BAGEL: a computational framework for identifying essential genes from pooled library screens.",10.1186/s12859-016-1015-8,https://doi.org/10.1186/s12859-016-1015-8 5 | 3,experiments/gene_essentiality_cancer/ai_messages/high_complexity--LGALS14,[4],"Blomen VA, Májek P, Jae LT, Bigenzahn JW, Nieuwenhuis J, Staring J, Sacco R, van Diemen FR, Olk N, Stukalov A, Marceau C, Janssen H, Carette JE, Bennett KL, Colinge J, Superti-Furga G, Brummelkamp TR. 2015. Science. Gene essentiality and synthetic lethality in haploid human cells.",10.1126/science.aac7557,https://doi.org/10.1126/science.aac7557 6 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--high_complexity--MRGPRX2.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | 0,experiments/gene_essentiality_cancer/ai_messages/high_complexity--MRGPRX2,[1],"Tatemoto K, Nozaki Y, Tsuda R, et al. 2006. Proc Natl Acad Sci U S A. Peptide histidine isoleucine and tryptase stimulate the release of histamine from human skin mast cells through activation of Mas-related gene X2.",10.3410/f.718012742.793480350,https://doi.org/10.3410/f.718012742.793480350 3 | 1,experiments/gene_essentiality_cancer/ai_messages/high_complexity--MRGPRX2,[2],"Lembo PM, Grazzini E, Groblewski T, et al. 2002. Proc Natl Acad Sci U S A. Proenkephalin A gene products activate a new family of sensory neuron--specific GPCRs.",10.1038/nn815,https://doi.org/10.1038/nn815 4 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--high_complexity--OR3A3.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | 0,experiments/gene_essentiality_cancer/ai_messages/high_complexity--OR3A3,[1],"Buck L, Axel R. 1991. Cell. A novel multigene family may encode odorant receptors: a molecular basis for odor recognition.",10.1016/0092-8674(91)90418-x,https://doi.org/10.1016/0092-8674(91)90418-x 3 | 1,experiments/gene_essentiality_cancer/ai_messages/high_complexity--OR3A3,[2],"Flegel C, Manteniotis S, Osthold S, Hatt H, Gisselmann G. 2013. Frontiers in Oncology. Expression profile of ectopic olfactory receptors determined by deep sequencing.",10.1371/journal.pone.0055368,https://doi.org/10.1371/journal.pone.0055368 4 | 2,experiments/gene_essentiality_cancer/ai_messages/high_complexity--OR3A3,[3],"Hart T, Chandrashekhar M, Aregger M, Steinhart Z, Brown KR, MacLeod G, Mis M, Zimmermann M, Fradet-Turcotte A, Sun S, Mero P, Dirks P, Sidhu S, Roth FP, Rissland OS, Durocher D, Angers S, Moffat J. 2015. Science. High-Resolution CRISPR Screens Reveal Fitness Genes and Genotype-Specific Cancer Liabilities.",10.1016/j.cell.2015.11.015,https://doi.org/10.1016/j.cell.2015.11.015 5 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--high_complexity--OR5T2.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | 0,experiments/gene_essentiality_cancer/ai_messages/high_complexity--OR5T2,[1],"Buck L, Axel R. 1991. Cell. A novel multigene family may encode odorant receptors: a molecular basis for odor recognition.",10.1016/0092-8674(91)90418-x,https://doi.org/10.1016/0092-8674(91)90418-x 3 | 1,experiments/gene_essentiality_cancer/ai_messages/high_complexity--OR5T2,[2],"Flegel C, Manteniotis S, Osthold S, Hatt H, Gisselmann G. 2013. Frontiers in Physiology. Expression profile of ectopic olfactory receptors determined by deep sequencing.",10.1371/journal.pone.0055368,https://doi.org/10.1371/journal.pone.0055368 4 | 2,experiments/gene_essentiality_cancer/ai_messages/high_complexity--OR5T2,[3],"Hart T, Chandrashekhar M, Aregger M, Steinhart Z, Brown KR, MacLeod G, Mis M, Zimmermann M, Fradet-Turcotte A, Sun S, Mero P, Dirks P, Sidhu S, Roth FP, Rissland OS, Durocher D, Angers S, Moffat J. 2015. Science. High-Resolution CRISPR Screens Reveal Fitness Genes and Genotype-Specific Cancer Liabilities.",10.1016/j.cell.2015.11.015,https://doi.org/10.1016/j.cell.2015.11.015 5 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--high_complexity--POLR3H.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | 0,experiments/gene_essentiality_cancer/ai_messages/high_complexity--POLR3H,[1],"Hu, P., Wu, S., & Sun, Y. (2002). The Journal of Biological Chemistry, The 14.5-kDa Subunit of Human Translation Initiation Factor eIF3 Is the Homolog of the Yeast eIF3 Subunit p.",10.1074/jbc.274.39.27567,https://doi.org/10.1074/jbc.274.39.27567 3 | 1,experiments/gene_essentiality_cancer/ai_messages/high_complexity--POLR3H,[2],"White, R. J. (2005). Nature Reviews Molecular Cell Biology, RNA polymerases I and III, growth control and cancer.",10.1038/nrm1551,https://doi.org/10.1038/nrm1551 4 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--high_complexity--PSMA7.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | 0,experiments/gene_essentiality_cancer/ai_messages/high_complexity--PSMA7,[1],"Tomko RJ Jr, Hochstrasser M. 2013. Molecular Biology and Evolution. Molecular architecture and assembly of the eukaryotic proteasome.",10.1146/annurev-biochem-060410-150257,https://doi.org/10.1146/annurev-biochem-060410-150257 3 | 1,experiments/gene_essentiality_cancer/ai_messages/high_complexity--PSMA7,[2],"Ciechanover A, Kwon YT. 2015. Nature Reviews Molecular Cell Biology. Protein quality control by molecular chaperones in neurodegeneration.",10.3389/fnins.2017.00185,https://doi.org/10.3389/fnins.2017.00185 4 | 2,experiments/gene_essentiality_cancer/ai_messages/high_complexity--PSMA7,[3],Adams J. 2004. Nature Reviews Cancer. The proteasome: a suitable antineoplastic target.,10.1038/nrc1361,https://doi.org/10.1038/nrc1361 5 | 3,experiments/gene_essentiality_cancer/ai_messages/high_complexity--PSMA7,[4],"Walter P, Ron D. 2011. Nature Reviews Molecular Cell Biology. The unfolded protein response: from stress pathway to homeostatic regulation.",10.1126/science.1209038,https://doi.org/10.1126/science.1209038 6 | 4,experiments/gene_essentiality_cancer/ai_messages/high_complexity--PSMA7,[5],"Tabas I, Ron D. 2011. Nature Cell Biology. Integrating the mechanisms of apoptosis induced by endoplasmic reticulum stress.",10.1038/ncb0311-184,https://doi.org/10.1038/ncb0311-184 7 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--high_complexity--SLC25A2.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | 0,experiments/gene_essentiality_cancer/ai_messages/high_complexity--SLC25A2,[1],"Palmieri F. (2013) The mitochondrial transporter family SLC25: identification, properties and physiopathology. Molecular Aspects of Medicine 34(2-3): 465-484.",10.1016/j.mam.2012.05.005,https://doi.org/10.1016/j.mam.2012.05.005 3 | 1,experiments/gene_essentiality_cancer/ai_messages/high_complexity--SLC25A2,[2],Pegg AE. (2009) Mammalian polyamine metabolism and function. IUBMB Life 61(9): 880-894.,10.1002/iub.230,https://doi.org/10.1002/iub.230 4 | 2,experiments/gene_essentiality_cancer/ai_messages/high_complexity--SLC25A2,[3],"Casero RA Jr, Marton LJ. (2007) Targeting polyamine metabolism and function in cancer and other hyperproliferative diseases. Nature Reviews Drug Discovery 6(5): 373-390.",10.1038/nrd2243,https://doi.org/10.1038/nrd2243 5 | 3,experiments/gene_essentiality_cancer/ai_messages/high_complexity--SLC25A2,[4],Seiler N. (2003) Thirty years of polyamine-related approaches to cancer therapy. Retrospect and prospect. Part 1. Selective enzyme inhibitors. Current Drug Targets 4(7): 537-564.,10.2174/1389450033490885,https://doi.org/10.2174/1389450033490885 6 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--high_complexity--TMPRSS11F.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | 0,experiments/gene_essentiality_cancer/ai_messages/high_complexity--TMPRSS11F,[1],"Szabo R, Bugge TH. (2011) Membrane-anchored serine proteases in vertebrate cell and developmental biology. Annu Rev Cell Dev Biol. 27:213-35.",10.1146/annurev-cellbio-092910-154247,https://doi.org/10.1146/annurev-cellbio-092910-154247 3 | 1,experiments/gene_essentiality_cancer/ai_messages/high_complexity--TMPRSS11F,[2],"Antalis TM, Bugge TH, Wu Q. (2011) Membrane-anchored serine proteases in health and disease. Prog Mol Biol Transl Sci. 99:1-50.",10.1016/b978-0-12-385504-6.00001-4,https://doi.org/10.1016/b978-0-12-385504-6.00001-4 4 | 2,experiments/gene_essentiality_cancer/ai_messages/high_complexity--TMPRSS11F,[3],"Hart T, Moffat J. (2016) BAGEL: a computational framework for identifying essential genes from pooled library screens. BMC Bioinformatics. 17:164.",10.1186/s12859-016-1015-8,https://doi.org/10.1186/s12859-016-1015-8 5 | 3,experiments/gene_essentiality_cancer/ai_messages/high_complexity--TMPRSS11F,[4],"Blomen VA, Májek P, Jae LT, Bigenzahn JW, Nieuwenhuis J, Staring J, Sacco R, van Diemen FR, Olk N, Stukalov A, Marceau C, Janssen H, Carette JE, Bennett KL, Colinge J, Superti-Furga G, Brummelkamp TR. (2015) Gene essentiality and synthetic lethality in haploid human cells. Science. 350:1092-6.",10.1126/science.aac7557,https://doi.org/10.1126/science.aac7557 6 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/gene_essentiality_cancer--high_complexity--ZP2.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | 0,experiments/gene_essentiality_cancer/ai_messages/high_complexity--ZP2,[1],"Lefièvre L, Conner SJ, Salpekar A, et al. 2004. Human Reproduction. Four zona pellucida glycoproteins are expressed in the human.",10.1093/humrep/deh301,https://doi.org/10.1093/humrep/deh301 3 | 1,experiments/gene_essentiality_cancer/ai_messages/high_complexity--ZP2,[2],"Rankin TL, Coleman JS, Epifano O, et al. 2003. Developmental Biology. Fertility and taxon-specific sperm binding persist after replacement of mouse sperm receptors with human homologs.",10.1016/s1534-5807(03)00195-3,https://doi.org/10.1016/s1534-5807(03)00195-3 4 | 2,experiments/gene_essentiality_cancer/ai_messages/high_complexity--ZP2,[3],"Domcke S, Sinha R, Levine DA, et al. 2013. Nature Communications. Evaluating cell lines as tumour models by comparison of genomic profiles.",10.1038/ncomms3126,https://doi.org/10.1038/ncomms3126 5 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/glioblastoma_survival--baseline--generated_EGFR_MGMT_low_methylated.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/glioblastoma_survival--baseline--generated_NF1_MGMT_medium_methylated.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/glioblastoma_survival--baseline--generated_PIK3CA_MGMT_high_methylated.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/glioblastoma_survival--baseline--generated_PTEN_EGFR_MGMT_high_methylated.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/glioblastoma_survival--baseline--generated_PTEN_MGMT_medium_methylated.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/glioblastoma_survival--baseline--generated_PTEN_MUC16_MGMT_medium_methylated.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/glioblastoma_survival--baseline--generated_PTEN_NF1_MGMT_low_methylated.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/glioblastoma_survival--baseline--generated_PTEN_TP53_NF1_RB1_MGMT_medium_methylated.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/glioblastoma_survival--baseline--generated_TP53_MGMT_medium_methylated.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/glioblastoma_survival--baseline--generated_TP53_PIK3CA_MGMT_medium_methylated.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/glioblastoma_survival--high_complexity--generated_EGFR_MGMT_low_methylated.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | 0,experiments/glioblastoma_survival/ai_messages/high_complexity--generated_EGFR_MGMT_low_methylated,[1],"Brennan CW, et al. 2013. Cell. The somatic genomic landscape of glioblastoma.",10.7717/peerj.13105/table-1,https://doi.org/10.7717/peerj.13105/table-1 3 | 1,experiments/glioblastoma_survival/ai_messages/high_complexity--generated_EGFR_MGMT_low_methylated,[2],"Hegi ME, et al. 2005. N Engl J Med. MGMT gene silencing and benefit from temozolomide in glioblastoma.",10.3410/f.718384246.793495041,https://doi.org/10.3410/f.718384246.793495041 4 | 2,experiments/glioblastoma_survival/ai_messages/high_complexity--generated_EGFR_MGMT_low_methylated,[3],"Stupp R, et al. 2005. N Engl J Med. Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma.",10.1097/01.cot.0000289242.47980.f9,https://doi.org/10.1097/01.cot.0000289242.47980.f9 5 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/glioblastoma_survival--high_complexity--generated_PIK3CA_MGMT_high_methylated.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | 0,experiments/glioblastoma_survival/ai_messages/high_complexity--generated_PIK3CA_MGMT_high_methylated,[1],"Samuels Y, et al. 2004. Science. Mutant PIK3CA promotes cell growth and invasion of human cancer cells.",10.3410/f.1026480.335881,https://doi.org/10.3410/f.1026480.335881 3 | 1,experiments/glioblastoma_survival/ai_messages/high_complexity--generated_PIK3CA_MGMT_high_methylated,[2],"Hegi ME, et al. 2005. N Engl J Med. MGMT gene silencing and benefit from temozolomide in glioblastoma.",10.3410/f.718384246.793495041,https://doi.org/10.3410/f.718384246.793495041 4 | 2,experiments/glioblastoma_survival/ai_messages/high_complexity--generated_PIK3CA_MGMT_high_methylated,[3],"Zhao HF, et al. 2017. Oncotarget. PIK3CA mutations promote glioma cell invasion and migration.",10.18632/oncotarget.23931,https://doi.org/10.18632/oncotarget.23931 5 | 3,experiments/glioblastoma_survival/ai_messages/high_complexity--generated_PIK3CA_MGMT_high_methylated,[4],"Roos WP, et al. 2007. Oncogene. Apoptosis in malignant glioma cells triggered by the temozolomide-induced DNA lesion O6-methylguanine.",10.1038/sj.onc.1209785,https://doi.org/10.1038/sj.onc.1209785 6 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/glioblastoma_survival--high_complexity--generated_PTEN_TP53_NF1_RB1_MGMT_medium_methylated.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | 0,experiments/glioblastoma_survival/ai_messages/high_complexity--generated_PTEN_TP53_NF1_RB1_MGMT_medium_methylated,[1],"Stupp R, et al. 2005. New England Journal of Medicine. Radiotherapy plus Concomitant and Adjuvant Temozolomide for Glioblastoma.",10.1097/01.cot.0000289242.47980.f9,https://doi.org/10.1097/01.cot.0000289242.47980.f9 3 | 1,experiments/glioblastoma_survival/ai_messages/high_complexity--generated_PTEN_TP53_NF1_RB1_MGMT_medium_methylated,[2],"Furnari FB, et al. 2007. Genes & Development. Malignant astrocytic glioma: genetics, biology, and paths to treatment.",10.1101/gad.1596707,https://doi.org/10.1101/gad.1596707 4 | 2,experiments/glioblastoma_survival/ai_messages/high_complexity--generated_PTEN_TP53_NF1_RB1_MGMT_medium_methylated,[3],"Hegi ME, et al. 2005. New England Journal of Medicine. MGMT Gene Silencing and Benefit from Temozolomide in Glioblastoma.",10.1056/nejmoa043331,https://doi.org/10.1056/nejmoa043331 5 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/mouse_immunology--baseline--Cyanide.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/mouse_immunology--baseline--YUMM_1_7.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/mouse_immunology--high_complexity--YUMM_1_7.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | 0,experiments/mouse_immunology/ai_messages/high_complexity--YUMM_1_7,[1],"Meeth K, Wang JX, Micevic G, Damsky W, Bosenberg MW. 2016. The YUMM lines: a series of congenic mouse melanoma cell lines with defined genetic alterations. Pigment Cell Melanoma Res. 29(5):590-7.",10.1111/pcmr.12498,https://doi.org/10.1111/pcmr.12498 3 | 1,experiments/mouse_immunology/ai_messages/high_complexity--YUMM_1_7,[2],National Cancer Institute. 2021. Melanoma Treatment (PDQ®)–Patient Version. https://www.cancer.gov/types/skin/patient/melanoma-treatment-pdq,10.1093/jnci/96.18.1355,https://doi.org/10.1093/jnci/96.18.1355 4 | 2,experiments/mouse_immunology/ai_messages/high_complexity--YUMM_1_7,[3],"Vinay DS, Ryan EP, Pawelec G, Talib WH, Stagg J, Elkord E, Lichtor T, Decker WK, Whelan RL, Kumara HMCS, Signori E, Honoki K, Georgakilas AG, Amin A, Helferich WG, Boosani CS, Guha G, Ciriolo MR, Chen S, Mohammed SI, Azmi AS, Keith WN, Bilsland A, Bhakta D, Halicka D, Fujii H, Aquilano K, Ashraf SS, Nowsheen S, Yang X, Choi BK, Kwon BS. 2015. Immune evasion in cancer: Mechanistic basis and therapeutic strategies. Semin Cancer Biol. 35 Suppl:S185-S198.",10.1016/j.semcancer.2015.03.004,https://doi.org/10.1016/j.semcancer.2015.03.004 5 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/sepsis_treatment--baseline--sepsis_hyperinflammation.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/sepsis_treatment--baseline--sepsis_immunoparalysis.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/trained_immunity--baseline--tolerance_LPS.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/trained_immunity--baseline--training_LPS.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/cross_ref/trained_immunity--baseline--training_betaglucan.csv: -------------------------------------------------------------------------------- 1 | ,file,ref_number,ref_text,doi,doi_url 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/crc--baseline--crc_apc_impact_2020_0.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/crc--baseline--crc_apc_impact_2020_1.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/crc--baseline--crc_apc_impact_2020_10.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/crc--baseline--crc_apc_impact_2020_11.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/crc--baseline--crc_apc_impact_2020_12.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/crc--baseline--crc_apc_impact_2020_13.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/crc--baseline--crc_apc_impact_2020_14.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/crc--baseline--crc_apc_impact_2020_15.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/crc--baseline--crc_apc_impact_2020_16.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/crc--baseline--crc_apc_impact_2020_17.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/crc--baseline--crc_apc_impact_2020_18.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/crc--baseline--crc_apc_impact_2020_19.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/crc--baseline--crc_apc_impact_2020_20.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/crc--baseline--crc_apc_impact_2020_3.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/crc--baseline--crc_apc_impact_2020_5.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/crc--baseline--crc_apc_impact_2020_6.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/crc--baseline--crc_apc_impact_2020_8.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/crc--baseline--crc_apc_impact_2020_9.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/crc--high_complexity--crc_apc_impact_2020_14.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | experiments/crc/ai_messages/high_complexity--crc_apc_impact_2020_14,[1],"Siegel RL, Miller KD, Jemal A. 2020. CA Cancer J Clin. Cancer statistics, 2020.",10.3322/caac.21590,https://doi.org/10.3322/caac.21590,"Cancer statistics, 2020",Siegel,100,100 3 | experiments/crc/ai_messages/high_complexity--crc_apc_impact_2020_14,[2],"Van Cutsem E, Cervantes A, Nordlinger B, Arnold D. 2014. Ann Oncol. Metastatic colorectal cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up.",10.1093/annonc/mdu260,https://doi.org/10.1093/annonc/mdu260,"Metastatic colorectal cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up",Van Cutsem,100,100 4 | experiments/crc/ai_messages/high_complexity--crc_apc_impact_2020_14,[3],"Hanahan D, Weinberg RA. 2011. Cell. Hallmarks of cancer: the next generation.",10.1016/j.cell.2011.02.013,https://doi.org/10.1016/j.cell.2011.02.013,Hallmarks of Cancer: The Next Generation,Hanahan,100,100 5 | experiments/crc/ai_messages/high_complexity--crc_apc_impact_2020_14,[4],"Mehlen P, Puisieux A. 2006. Nat Rev Cancer. Metastasis: a question of life or death.",10.1038/nrc1886,https://doi.org/10.1038/nrc1886,Metastasis: a question of life or death,Mehlen,100,100 6 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--baseline--AGAP5.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--baseline--ANKLE2.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--baseline--AQP8.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--baseline--CCT5.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--baseline--DCTN3.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--baseline--DHX16.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--baseline--DSPP.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--baseline--EIF2B3.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--baseline--FAM71C.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--baseline--GK2.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--baseline--GPR78.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--baseline--GTF3C1.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--baseline--H2BC1.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--baseline--HAUS4.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--baseline--HNRNPK.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--baseline--HNRNPL.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--baseline--HSPD1.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--baseline--IFNA10.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--baseline--IL12B.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--baseline--IL17F.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--baseline--KIF2B.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--baseline--KRT25.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--baseline--KRT26.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--baseline--KRT82.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--baseline--KRTAP9-3.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--baseline--LGALS14.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--baseline--MRGPRX2.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--baseline--NDST4.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--baseline--NDUFB3.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--baseline--NOC3L.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--baseline--OPA1.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--baseline--OR3A3.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--baseline--OR52W1.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--baseline--OR5T2.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--baseline--POLR3H.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--baseline--PRPF19.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--baseline--PSMA7.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--baseline--PSMB7.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--baseline--RBX1.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--baseline--RPL27.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--baseline--RPL37A.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--baseline--RPL7A.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--baseline--S100A7A.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--baseline--SDR9C7.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--baseline--SLC25A2.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--baseline--SS18L2.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--baseline--SUPT16H.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--baseline--TIMM29.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--baseline--TMPRSS11F.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--baseline--ZP2.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--high_complexity--AGAP5.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | experiments/gene_essentiality_cancer/ai_messages/high_complexity--AGAP5,[1],"Nie, Z., & Randazzo, P. A. (2006). Arf GAPs and membrane traffic. Journal of Cell Science, 119(Pt 7), 1203-1211.",10.1242/jcs.02924,https://doi.org/10.1242/jcs.02924,Arf GAPs and membrane traffic,Nie,100,100 3 | experiments/gene_essentiality_cancer/ai_messages/high_complexity--AGAP5,[2],"Hart, T., Chandrashekhar, M., Aregger, M., Steinhart, Z., Brown, K. R., MacLeod, G., ... & Moffat, J. (2015). High-resolution CRISPR screens reveal fitness genes and genotype-specific cancer liabilities. Cell, 163(6), 1515-1526.",10.1016/j.cell.2015.11.015,https://doi.org/10.1016/j.cell.2015.11.015,High-Resolution CRISPR Screens Reveal Fitness Genes and Genotype-Specific Cancer Liabilities,Hart,100,100 4 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--high_complexity--DSPP.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | experiments/gene_essentiality_cancer/ai_messages/high_complexity--DSPP,[1],"Yamakoshi Y, Hu JC, Fukae M, Zhang H, Simmer JP. 2005. Journal of Biological Chemistry. Dentin Sialophosphoprotein is Processed by MMP-2 and MMP-20 in vitro and in vivo.",10.1074/jbc.m607767200,https://doi.org/10.1074/jbc.m607767200,Dentin Sialophosphoprotein Is Processed by MMP-2 and MMP-20 in Vitro and in Vivo,Yamakoshi,100,100 3 | experiments/gene_essentiality_cancer/ai_messages/high_complexity--DSPP,[2],"Ogbureke KU, Fisher LW. 2004. Journal of Histochemistry & Cytochemistry. Expression of SIBLINGs and their partner MMPs in salivary glands.",10.1177/154405910408300902,https://doi.org/10.1177/154405910408300902,Expression of SIBLINGs and Their Partner MMPs in Salivary Glands,Ogbureke,100,100 4 | experiments/gene_essentiality_cancer/ai_messages/high_complexity--DSPP,[3],Masters JR. 2000. British Journal of Cancer. Human cancer cell lines: fact and fantasy.,10.1038/35043102,https://doi.org/10.1038/35043102,Human cancer cell lines: fact and fantasy,Masters,100,100 5 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--high_complexity--GK2.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | experiments/gene_essentiality_cancer/ai_messages/high_complexity--GK2,[1],"Bairoch A, Apweiler R. 2000. The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000. Nucleic Acids Res. 28(1):45-48.",10.1093/nar/28.1.45,https://doi.org/10.1093/nar/28.1.45,The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000,Bairoch,100,100 3 | experiments/gene_essentiality_cancer/ai_messages/high_complexity--GK2,[2],"Nelson DL, Cox MM. 2017. Lehninger Principles of Biochemistry. 7th ed. W.H. Freeman and Company.",10.1002/bmb.2005.494033010419,https://doi.org/10.1002/bmb.2005.494033010419,"Lehninger principles of biochemistry (4th ed.): Nelson, D., and Cox, M.",Boyle,11,93 4 | experiments/gene_essentiality_cancer/ai_messages/high_complexity--GK2,[3],"Hanahan D, Weinberg RA. 2011. Hallmarks of cancer: the next generation. Cell. 144(5):646-674.",10.1016/j.cell.2011.02.013,https://doi.org/10.1016/j.cell.2011.02.013,Hallmarks of Cancer: The Next Generation,Hanahan,100,100 5 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--high_complexity--GPR78.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | experiments/gene_essentiality_cancer/ai_messages/high_complexity--GPR78,[1],"Fredriksson R, Lagerström MC, Lundin LG, Schiöth HB. 2003. The G-protein-coupled receptors in the human genome form five main families. Phylogenetic analysis, paralogon groups, and fingerprints. Molecular Pharmacology 63(6):1256-1272.",10.1124/mol.63.6.1256,https://doi.org/10.1124/mol.63.6.1256,"The G-Protein-Coupled Receptors in the Human Genome Form Five Main Families. Phylogenetic Analysis, Paralogon Groups, and Fingerprints",Fredriksson,100,100 3 | experiments/gene_essentiality_cancer/ai_messages/high_complexity--GPR78,[2],"Hart T, Chandrashekhar M, Aregger M, Steinhart Z, Brown KR, MacLeod G, Mis M, Zimmermann M, Fradet-Turcotte A, Sun S, Mero P, Dirks P, Sidhu S, Roth FP, Rissland OS, Durocher D, Angers S, Moffat J. 2015. High-Resolution CRISPR Screens Reveal Fitness Genes and Genotype-Specific Cancer Liabilities. Cell 163(6):1515-1526.",10.1016/j.cell.2015.11.015,https://doi.org/10.1016/j.cell.2015.11.015,High-Resolution CRISPR Screens Reveal Fitness Genes and Genotype-Specific Cancer Liabilities,Hart,100,100 4 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--high_complexity--KRT25.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | experiments/gene_essentiality_cancer/ai_messages/high_complexity--KRT25,[1],"Schweizer J, Bowden PE, Coulombe PA, et al. 2006. J Cell Biol. New consensus nomenclature for mammalian keratins.",10.1083/jcb.200603161,https://doi.org/10.1083/jcb.200603161,New consensus nomenclature for mammalian keratins,Schweizer,100,100 3 | experiments/gene_essentiality_cancer/ai_messages/high_complexity--KRT25,[2],"Moll R, Divo M, Langbein L. 2008. Histochem Cell Biol. The human keratins: biology and pathology.",10.1007/s00418-008-0435-6,https://doi.org/10.1007/s00418-008-0435-6,The human keratins: biology and pathology,Moll,100,100 4 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--high_complexity--KRT26.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | experiments/gene_essentiality_cancer/ai_messages/high_complexity--KRT26,[1],"Moll R, Divo M, Langbein L. 2008. The human keratins: biology and pathology. Histochemistry and Cell Biology.",10.1007/s00418-008-0435-6,https://doi.org/10.1007/s00418-008-0435-6,The human keratins: biology and pathology,Moll,100,100 3 | experiments/gene_essentiality_cancer/ai_messages/high_complexity--KRT26,[2],"Shimomura Y, Wajid M, Petukhova L, Kurban M, Christiano AM. 2010. Autosomal-dominant woolly hair resulting from disruption of keratin 74 (KRT74), a potential determinant of human hair texture. American Journal of Human Genetics.",10.1016/j.ajhg.2010.02.025,https://doi.org/10.1016/j.ajhg.2010.02.025,"Autosomal-Dominant Woolly Hair Resulting from Disruption of Keratin 74 (KRT74), a Potential Determinant of Human Hair Texture",Shimomura,100,100 4 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--high_complexity--KRT82.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | experiments/gene_essentiality_cancer/ai_messages/high_complexity--KRT82,[1],"Schweizer J, Bowden PE, Coulombe PA, et al. 2006. J Cell Biol. New consensus nomenclature for mammalian keratins.",10.1083/jcb.200603161,https://doi.org/10.1083/jcb.200603161,New consensus nomenclature for mammalian keratins,Schweizer,100,100 3 | experiments/gene_essentiality_cancer/ai_messages/high_complexity--KRT82,[2],"Coulombe PA, Omary MB. 2002. Nat Rev Mol Cell Biol. 'Hard' and 'soft' principles defining the structure, function and regulation of keratin intermediate filaments.",10.1016/s0955-0674(01)00301-5,https://doi.org/10.1016/s0955-0674(01)00301-5,"‘Hard’ and ‘soft’ principles defining the structure, function and regulation of keratin intermediate filaments",Coulombe,100,100 4 | experiments/gene_essentiality_cancer/ai_messages/high_complexity--KRT82,[3],"Langbein L, Rogers MA, Winter H, et al. 2001. J Biol Chem. The catalog of human hair keratins. I. Expression of the nine type I members in the hair follicle.",10.1074/jbc.m103305200,https://doi.org/10.1074/jbc.m103305200,The Catalog of Human Hair Keratins,Langbein,100,100 5 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--high_complexity--KRTAP9-3.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | experiments/gene_essentiality_cancer/ai_messages/high_complexity--KRTAP9-3,[1],"Shimomura Y, Wajid M, Petukhova L, Kurban M, Christiano AM. 2010. Human Molecular Genetics. Mutations in the keratin-associated protein 9-3 (KRTAP9-3) gene underlie autosomal dominant woolly hair.",10.1016/j.ajhg.2010.02.025,https://doi.org/10.1016/j.ajhg.2010.02.025,"Autosomal-Dominant Woolly Hair Resulting from Disruption of Keratin 74 (KRT74), a Potential Determinant of Human Hair Texture",Shimomura,100,56 3 | experiments/gene_essentiality_cancer/ai_messages/high_complexity--KRTAP9-3,[2],"Rogers MA, Langbein L, Praetzel-Wunder S, Winter H, Schweizer J. 2006. Journal of Investigative Dermatology. Human hair keratin-associated proteins (KAPs).",10.1016/s0074-7696(06)51006-x,https://doi.org/10.1016/s0074-7696(06)51006-x,Human Hair Keratin‐Associated Proteins (KAPs),Rogers,100,100 4 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--high_complexity--MRGPRX2.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | experiments/gene_essentiality_cancer/ai_messages/high_complexity--MRGPRX2,[1],"Tatemoto K, Nozaki Y, Tsuda R, et al. 2006. Proc Natl Acad Sci U S A. Peptide histidine isoleucine and tryptase stimulate the release of histamine from human skin mast cells through activation of Mas-related gene X2.",10.3410/f.718012742.793480350,https://doi.org/10.3410/f.718012742.793480350,Faculty Opinions recommendation of β-Defensins activate human mast cells via Mas-related gene X2.,Stevens,6,58 3 | experiments/gene_essentiality_cancer/ai_messages/high_complexity--MRGPRX2,[2],"Lembo PM, Grazzini E, Groblewski T, et al. 2002. Proc Natl Acad Sci U S A. Proenkephalin A gene products activate a new family of sensory neuron--specific GPCRs.",10.1038/nn815,https://doi.org/10.1038/nn815,Proenkephalin A gene products activate a new family of sensory neuron–specific GPCRs,Lembo,100,100 4 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--high_complexity--POLR3H.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | experiments/gene_essentiality_cancer/ai_messages/high_complexity--POLR3H,[1],"Hu, P., Wu, S., & Sun, Y. (2002). The Journal of Biological Chemistry, The 14.5-kDa Subunit of Human Translation Initiation Factor eIF3 Is the Homolog of the Yeast eIF3 Subunit p.",10.1074/jbc.274.39.27567,https://doi.org/10.1074/jbc.274.39.27567,The Saccharomyces cerevisiae HCR1 Gene Encoding a Homologue of the p35 Subunit of Human Translation Initiation Factor 3 (eIF3) Is a High Copy Suppressor of a Temperature-sensitive Mutation in the Rpg1p Subunit of Yeast eIF3,Valášek,6,66 3 | experiments/gene_essentiality_cancer/ai_messages/high_complexity--POLR3H,[2],"White, R. J. (2005). Nature Reviews Molecular Cell Biology, RNA polymerases I and III, growth control and cancer.",10.1038/nrm1551,https://doi.org/10.1038/nrm1551,"RNA polymerases I and III, growth control and cancer",White,100,100 4 | -------------------------------------------------------------------------------- /reference_analysis/matched/gene_essentiality_cancer--high_complexity--ZP2.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | experiments/gene_essentiality_cancer/ai_messages/high_complexity--ZP2,[1],"Lefièvre L, Conner SJ, Salpekar A, et al. 2004. Human Reproduction. Four zona pellucida glycoproteins are expressed in the human.",10.1093/humrep/deh301,https://doi.org/10.1093/humrep/deh301,Four zona pellucida glycoproteins are expressed in the human,Lefievre,13,100 3 | experiments/gene_essentiality_cancer/ai_messages/high_complexity--ZP2,[2],"Rankin TL, Coleman JS, Epifano O, et al. 2003. Developmental Biology. Fertility and taxon-specific sperm binding persist after replacement of mouse sperm receptors with human homologs.",10.1016/s1534-5807(03)00195-3,https://doi.org/10.1016/s1534-5807(03)00195-3,Fertility and Taxon-Specific Sperm Binding Persist after Replacement of Mouse Sperm Receptors with Human Homologs,Rankin,100,100 4 | experiments/gene_essentiality_cancer/ai_messages/high_complexity--ZP2,[3],"Domcke S, Sinha R, Levine DA, et al. 2013. Nature Communications. Evaluating cell lines as tumour models by comparison of genomic profiles.",10.1038/ncomms3126,https://doi.org/10.1038/ncomms3126,Evaluating cell lines as tumour models by comparison of genomic profiles,Domcke,100,100 5 | -------------------------------------------------------------------------------- /reference_analysis/matched/glioblastoma_survival--baseline--generated_EGFR_MGMT_low_methylated.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/glioblastoma_survival--baseline--generated_NF1_MGMT_medium_methylated.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/glioblastoma_survival--baseline--generated_PIK3CA_MGMT_high_methylated.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/glioblastoma_survival--baseline--generated_PTEN_EGFR_MGMT_high_methylated.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/glioblastoma_survival--baseline--generated_PTEN_MGMT_medium_methylated.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/glioblastoma_survival--baseline--generated_PTEN_MUC16_MGMT_medium_methylated.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/glioblastoma_survival--baseline--generated_PTEN_NF1_MGMT_low_methylated.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/glioblastoma_survival--baseline--generated_PTEN_TP53_NF1_RB1_MGMT_medium_methylated.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/glioblastoma_survival--baseline--generated_TP53_MGMT_medium_methylated.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/glioblastoma_survival--baseline--generated_TP53_PIK3CA_MGMT_medium_methylated.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/glioblastoma_survival--high_complexity--generated_EGFR_MGMT_low_methylated.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | experiments/glioblastoma_survival/ai_messages/high_complexity--generated_EGFR_MGMT_low_methylated,[1],"Brennan CW, et al. 2013. Cell. The somatic genomic landscape of glioblastoma.",10.7717/peerj.13105/table-1,https://doi.org/10.7717/peerj.13105/table-1,"Table 1: Crown width-DBH (CW-D) candidate functions considered in the analysis (Fu et al., 2013; Raptis et al., 2018; Wang et al., 2021).",,0,43 3 | experiments/glioblastoma_survival/ai_messages/high_complexity--generated_EGFR_MGMT_low_methylated,[2],"Hegi ME, et al. 2005. N Engl J Med. MGMT gene silencing and benefit from temozolomide in glioblastoma.",10.3410/f.718384246.793495041,https://doi.org/10.3410/f.718384246.793495041,Faculty Opinions recommendation of MGMT gene silencing and benefit from temozolomide in glioblastoma.,Gilbert,10,80 4 | experiments/glioblastoma_survival/ai_messages/high_complexity--generated_EGFR_MGMT_low_methylated,[3],"Stupp R, et al. 2005. N Engl J Med. Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma.",10.1097/01.cot.0000289242.47980.f9,https://doi.org/10.1097/01.cot.0000289242.47980.f9,“Radiotherapy plus Concomitant and Adjuvant Temozolomide for Glioblastoma”,Stupp,100,100 5 | -------------------------------------------------------------------------------- /reference_analysis/matched/glioblastoma_survival--high_complexity--generated_PTEN_TP53_NF1_RB1_MGMT_medium_methylated.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | experiments/glioblastoma_survival/ai_messages/high_complexity--generated_PTEN_TP53_NF1_RB1_MGMT_medium_methylated,[1],"Stupp R, et al. 2005. New England Journal of Medicine. Radiotherapy plus Concomitant and Adjuvant Temozolomide for Glioblastoma.",10.1097/01.cot.0000289242.47980.f9,https://doi.org/10.1097/01.cot.0000289242.47980.f9,“Radiotherapy plus Concomitant and Adjuvant Temozolomide for Glioblastoma”,Stupp,100,100 3 | experiments/glioblastoma_survival/ai_messages/high_complexity--generated_PTEN_TP53_NF1_RB1_MGMT_medium_methylated,[2],"Furnari FB, et al. 2007. Genes & Development. Malignant astrocytic glioma: genetics, biology, and paths to treatment.",10.1101/gad.1596707,https://doi.org/10.1101/gad.1596707,"Malignant astrocytic glioma: genetics, biology, and paths to treatment",Furnari,100,100 4 | experiments/glioblastoma_survival/ai_messages/high_complexity--generated_PTEN_TP53_NF1_RB1_MGMT_medium_methylated,[3],"Hegi ME, et al. 2005. New England Journal of Medicine. MGMT Gene Silencing and Benefit from Temozolomide in Glioblastoma.",10.1056/nejmoa043331,https://doi.org/10.1056/nejmoa043331,MGMTGene Silencing and Benefit from Temozolomide in Glioblastoma,Hegi,100,98 5 | -------------------------------------------------------------------------------- /reference_analysis/matched/mouse_immunology--baseline--Cyanide.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/mouse_immunology--baseline--YUMM_1_7.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/sepsis_treatment--baseline--sepsis_hyperinflammation.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/sepsis_treatment--baseline--sepsis_immunoparalysis.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/trained_immunity--baseline--tolerance_LPS.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/trained_immunity--baseline--training_LPS.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /reference_analysis/matched/trained_immunity--baseline--training_betaglucan.csv: -------------------------------------------------------------------------------- 1 | file,ref_number,ref_text,doi,doi_url,titleDoi,firstAuthDoi,author_similarity,title_similarity 2 | -------------------------------------------------------------------------------- /simulateGPT_logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/OpenBioLink/SimulateGPT/dd1ba1a876888e6c03633b4b6aae9634df02932c/simulateGPT_logo.png -------------------------------------------------------------------------------- /src/rule_simulate.py: -------------------------------------------------------------------------------- 1 | from langchain.schema import AIMessage, HumanMessage, SystemMessage 2 | from utils import load_openai_api 3 | from pathlib import Path 4 | 5 | batch_chat, stream_chat = load_openai_api() 6 | 7 | request = [ 8 | SystemMessage(content=Path(snakemake.input.system_message).read_text()), 9 | HumanMessage(content=Path(snakemake.input.human_message).read_text()), 10 | ] 11 | result = stream_chat(request) 12 | 13 | out_path = Path(snakemake.output[0]) 14 | out_path.parent.mkdir(exist_ok=True) 15 | out_path.write_text(result.content) 16 | -------------------------------------------------------------------------------- /system_messages/baseline: -------------------------------------------------------------------------------- 1 | You are a science-based, comprehensive and mechanistic AI system with knowledge of biomedical processes across all levels of biology: molecular, cellular, organ, and organism. 2 | 3 | Your input consists of parameters describing a biomedical scenario and an outcome request. Based on the input, you infer the requested outcome. 4 | 5 | Format your response in YAML, following this schema: 6 | 7 | conclusion: 8 | outcome: 9 | explanation: 10 | --------------------------------------------------------------------------------