├── CODE_OF_CONDUCT.md ├── LICENSE ├── README.md └── publications.md /CODE_OF_CONDUCT.md: -------------------------------------------------------------------------------- 1 | # Contributor Covenant Code of Conduct 2 | 3 | ## Our Pledge 4 | 5 | In the interest of fostering an open and welcoming environment, we as 6 | contributors and maintainers pledge to making participation in our project and 7 | our community a harassment-free experience for everyone, regardless of age, body 8 | size, disability, ethnicity, sex characteristics, gender identity and expression, 9 | level of experience, education, socio-economic status, nationality, personal 10 | appearance, race, religion, or sexual identity and orientation. 11 | 12 | ## Our Standards 13 | 14 | Examples of behavior that contributes to creating a positive environment 15 | include: 16 | 17 | * Using welcoming and inclusive language 18 | * Being respectful of differing viewpoints and experiences 19 | * Gracefully accepting constructive criticism 20 | * Focusing on what is best for the community 21 | * Showing empathy towards other community members 22 | 23 | Examples of unacceptable behavior by participants include: 24 | 25 | * The use of sexualized language or imagery and unwelcome sexual attention or 26 | advances 27 | * Trolling, insulting/derogatory comments, and personal or political attacks 28 | * Public or private harassment 29 | * Publishing others' private information, such as a physical or electronic 30 | address, without explicit permission 31 | * Other conduct which could reasonably be considered inappropriate in a 32 | professional setting 33 | 34 | ## Our Responsibilities 35 | 36 | Project maintainers are responsible for clarifying the standards of acceptable 37 | behavior and are expected to take appropriate and fair corrective action in 38 | response to any instances of unacceptable behavior. 39 | 40 | Project maintainers have the right and responsibility to remove, edit, or 41 | reject comments, commits, code, wiki edits, issues, and other contributions 42 | that are not aligned to this Code of Conduct, or to ban temporarily or 43 | permanently any contributor for other behaviors that they deem inappropriate, 44 | threatening, offensive, or harmful. 45 | 46 | ## Scope 47 | 48 | This Code of Conduct applies both within project spaces and in public spaces 49 | when an individual is representing the project or its community. Examples of 50 | representing a project or community include using an official project e-mail 51 | address, posting via an official social media account, or acting as an appointed 52 | representative at an online or offline event. Representation of a project may be 53 | further defined and clarified by project maintainers. 54 | 55 | ## Enforcement 56 | 57 | Instances of abusive, harassing, or otherwise unacceptable behavior may be 58 | reported by contacting the project team at achursov@datomicsgroup.com. All 59 | complaints will be reviewed and investigated and will result in a response that 60 | is deemed necessary and appropriate to the circumstances. The project team is 61 | obligated to maintain confidentiality with regard to the reporter of an incident. 62 | Further details of specific enforcement policies may be posted separately. 63 | 64 | Project maintainers who do not follow or enforce the Code of Conduct in good 65 | faith may face temporary or permanent repercussions as determined by other 66 | members of the project's leadership. 67 | 68 | ## Attribution 69 | 70 | This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, 71 | available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html 72 | 73 | [homepage]: https://www.contributor-covenant.org 74 | 75 | For answers to common questions about this code of conduct, see 76 | https://www.contributor-covenant.org/faq 77 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2019 Andrey Chursov 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 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Deep Learning for Biomedicine 2 | This is a curated list of tutorials, projects, libraries, videos, papers, books and anything related to applications of Deep Learning to biomedicine. Feel free to make a pull request to contribute to this list. 3 | 4 | ## Table of contents 5 | - [Courses](#courses) 6 | - [Tutorials & Presentations](#tutorials) 7 | - [Projects](#projects) 8 | - [Libraries](#libraries) 9 | - [PyTorch](#pytorch) 10 | - [TensorFlow](#tensorflow) 11 | - [Publications](#publications) 12 | 13 | 14 | ## Courses 15 | - [Machine Learning for Computational Biology, MIT, Fall 2024](https://www.youtube.com/playlist?list=PLypiXJdtIca4gtioEPLIExlAKvu64z7rc) 16 | - [Lecture Notes](https://docs.google.com/document/d/17eG2nS0kM3RyxDZsxpLiSKaNjFLCsXvRXX3CisZvyjY/edit) 17 | - [Course Website](https://canvas.mit.edu/courses/33939) 18 | 19 | ## Tutorials & Presentations 20 | - [Interactive tutorial to build a convolutional neural network to discover DNA-binding motifs](https://colab.research.google.com/drive/17E4h5aAOioh5DiTo7MZg4hpL6Z_0FyWr) 21 | - [Martin Preusse, Gökcen Eraslan: "Deep modeling of DNA sequences with Python & Keras" (PyMunich 2016)](https://www.youtube.com/watch?v=0Zuqytgf6yY) 22 | - [James Zou: "Deep learning for genomics: Introduction and examples" (Computational Genomics Summer Institute 2017)](https://www.youtube.com/watch?v=JYt1IqdDAPc) 23 | - [Cory McLean: "Nucleus: TensorFlow toolkit for Genomics" (TensorFlow Dev Summit 2018)](https://www.youtube.com/watch?v=7wi9NdGh9oI) 24 | - [Lee Cooper: "Predicting Cancer Outcomes from Genomics and Histology with Deep Learning" (NCI Webinars 2018)](https://www.youtube.com/watch?v=X_rvcs3zEis) 25 | - [William Noble: "Machine learning methods for making sense of big genomic data" (Computational Genomics Winter Institute 2018)](https://www.youtube.com/watch?v=JzSf5AU9VVc) 26 | - [Avanti Shrikumar: "Not Just a Black Box: Interpretable Deep Learning for Genomics and Beyond" (NVIDIA GTC 2018)](https://www.youtube.com/watch?v=T8AMLwmy_vQ) 27 | - [Olga Troyanskaya: "The Science of Deep Learning" (National Academy of Sciences Arthur M. Sackler Colloquium 2019)](https://www.youtube.com/watch?v=94B8zGsF_JE) 28 | - [Peter Koo: "Interpretable convolutional networks for regulatory genomics" (Models, Inference and Algorithms Meeting 2019)](https://www.youtube.com/watch?v=nQqVxGzeYZs) 29 | 30 | ## Projects 31 | - [Kipoi: Model Zoo for Genomics](https://kipoi.org) 32 | 33 | ## Libraries 34 | ### PyTorch 35 | 36 | #### Selene 37 | - **2019-03** | Selene: a PyTorch-based deep learning library for sequence data | *Kathleen M. Chen, Evan M. Cofer, Jian Zhou & Olga G. Troyanskaya* | [Nature Methods](https://www.nature.com/articles/s41592-019-0360-8) 38 | - http://selene.flatironinstitute.org 39 | - https://github.com/FunctionLab/selene 40 | 41 | ### TensorFlow 42 | 43 | #### Pysster 44 | - **2018-09** | pysster: classification of biological sequences by learning sequence and structure motifs with convolutional neural networks | *Stefan Budach, Annalisa Marsico* | [Bioinformatics](https://academic.oup.com/bioinformatics/article/34/17/3035/4962494) 45 | - https://github.com/budach/pysster 46 | 47 | #### DragoNN 48 | - https://kundajelab.github.io/dragonn/ 49 | - https://github.com/kundajelab/dragonn 50 | 51 | ## [Publications](https://github.com/achursov/deepbio/blob/master/publications.md) 52 | -------------------------------------------------------------------------------- /publications.md: -------------------------------------------------------------------------------- 1 | ## Publications 2 | ### Research Papers 3 | 4 | #### Genomics 5 | - **2015-01** | DEEP: a general computational framework for predicting enhancers | *Dimitrios Kleftogiannis, Panos Kalnis, Vladimir B. Bajic* | [Nucleic Acids Research](https://academic.oup.com/nar/article/43/1/e6/2902605) 6 | - **2015-03** | DANN: a deep learning approach for annotating the pathogenicity of genetic variants | *Daniel Quang, Yifei Chen, Xiaohui Xie* | [Bioinformatics](https://academic.oup.com/bioinformatics/article/31/5/761/2748191) 7 | - **2015-07** | Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning | *Babak Alipanahi, Andrew Delong, Matthew T Weirauch & Brendan J Frey* | [Nature Biotechnology](https://www.nature.com/articles/nbt.3300) 8 | - **2015-08** | Predicting effects of noncoding variants with deep learning–based sequence model | *Jian Zhou & Olga G Troyanskaya* | [Nature Methods](https://www.nature.com/articles/nmeth.3547) 9 | - **2016-01** | Predicting DNA Methylation State of CpG Dinucleotide Using Genome Topological Features and Deep Networks | *Yiheng Wang, Tong Liu, Dong Xu, Huidong Shi, Chaoyang Zhang, Yin-Yuan Mo & Zheng Wang* | [Scientific Reports](https://www.nature.com/articles/srep19598) 10 | - **2016-02** | Gene expression inference with deep learning | *Yifei Chen, Yi Li, Rajiv Narayan, Aravind Subramanian, Xiaohui Xie* | [Bioinformatics](https://academic.oup.com/bioinformatics/article/32/12/1832/1743989) 11 | - **2016-05** | Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks | *David R. Kelley, Jasper Snoek and John L. Rinn* | [Genome Research](https://genome.cshlp.org/content/26/7/990.short) 12 | - **2016-06** | Convolutional neural network architectures for predicting DNA–protein binding | Haoyang Zeng, Matthew D. Edwards, Ge Liu, David K. Gifford | [Bioinformatics](https://academic.oup.com/bioinformatics/article/32/12/i121/2240609) 13 | - **2016-06** | DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences | *Daniel Quang, Xiaohui Xie* | [Nucleic Acids Research](https://academic.oup.com/nar/article/44/11/e107/2468300#96180312) 14 | - **2016-06** | PEDLA: predicting enhancers with a deep learning-based algorithmic framework | *Feng Liu, Hao Li, Chao Ren, Xiaochen Bo & Wenjie Shu* | [Scientific Reports](https://www.nature.com/articles/srep28517) 15 | - **2017-04** | DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning | *Christof Angermueller, Heather J. Lee, Wolf Reik & Oliver Stegle* | [Genome Biology](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-017-1189-z) 16 | - **2017-07** | Denoising genome-wide histone ChIP-seq with convolutional neural networks | *Pang Wei Koh, Emma Pierson, Anshul Kundaje* | [Bioinformatics](https://academic.oup.com/bioinformatics/article/33/14/i225/3953958) 17 | - **2017-12** | Predicting enhancers with deep convolutional neural networks | *Xu Min, Wanwen Zeng, Shengquan Chen, Ning Chen, Ting Chen & Rui Jiang* | [BMC Bioinformatics](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-017-1878-3) 18 | - **2017** | Deep motif dashboard: visualizing and understanding genomic sequences using deep neural networks | *JACK LANCHANTIN, RITAMBHARA SINGH, BEILUN WANG and YANJUN QI* | [Pacific Symposium on Biocomputing 2017](https://www.worldscientific.com/doi/abs/10.1142/9789813207813_0025) 19 | - **2018-01** | Deep Learning for Better Variant Calling for Cancer Diagnosis and Treatment | *Anand Ramachandran, Huiren Li, Eric Klee, Steven S. Lumetta, and Deming Chen* | [Proceedings of the 23rd Asia and South Pacific Design Automation Conference](https://dl.acm.org/citation.cfm?id=3201611) 20 | - **2018-05** | Genome-wide prediction of cis-regulatory regions using supervised deep learning methods | *Yifeng Li, Wenqiang Shi & Wyeth W. Wasserman* | [BMC Bioinformatics](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-018-2187-1) 21 | - **2018-07** | Predicting the clinical impact of human mutation with deep neural networks | *Laksshman Sundaram, Hong Gao, Samskruthi Reddy Padigepati, Jeremy F. McRae, Yanjun Li, Jack A. Kosmicki, Nondas Fritzilas, Jörg Hakenberg, Anindita Dutta, John Shon, Jinbo Xu, Serafim Batzoglou, Xiaolin Li & Kyle Kai-How Farh* | [Nature Genetics](https://www.nature.com/articles/s41588-018-0167-z) 22 | - **2018-07** | Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk | *Jian Zhou, Chandra L. Theesfeld, Kevin Yao, Kathleen M. Chen, Aaron K. Wong & Olga G. Troyanskaya* | [Nature Genetics](https://www.nature.com/articles/s41588-018-0160-6) 23 | - **2018-09** | A universal SNP and small-indel variant caller using deep neural networks | *Ryan Poplin, Pi-Chuan Chang, David Alexander, Scott Schwartz, Thomas Colthurst, Alexander Ku, Dan Newburger, Jojo Dijamco, Nam Nguyen, Pegah T Afshar, Sam S Gross, Lizzie Dorfman, Cory Y McLean & Mark A DePristo* | [Nature Biotechnology](https://www.nature.com/articles/nbt.4235) 24 | - **2018-11** | A deep learning approach to automate refinement of somatic variant calling from cancer sequencing data | *Benjamin J. Ainscough, Erica K. Barnell, Peter Ronning, Katie M. Campbell, Alex H. Wagner, Todd A. Fehniger, Gavin P. Dunn, Ravindra Uppaluri, Ramaswamy Govindan, Thomas E. Rohan, Malachi Griffith, Elaine R. Mardis, S. Joshua Swamidass & Obi L. Griffith* | [Nature Genetics](https://www.nature.com/articles/s41588-018-0257-y) 25 | - **2018-12** | The Unreasonable Effectiveness of Convolutional Neural Networks in Population Genetic Inference | *Lex Flagel, Yaniv Brandvain, Daniel R Schrider* | [Molecular Biology and Evolution](https://academic.oup.com/mbe/article/36/2/220/5229930) 26 | - **2019-03** | A multi-task convolutional deep neural network for variant calling in single molecule sequencing | *Ruibang Luo, Fritz J. Sedlazeck, Tak-Wah Lam & Michael C. Schatz* | [Nature Communications](https://www.nature.com/articles/s41467-019-09025-z) 27 | - **2019-05** | Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk | *Jian Zhou, Christopher Y. Park, Chandra L. Theesfeld, Aaron K. Wong, Yuan Yuan, Claudia Scheckel, John J. Fak, Julien Funk, Kevin Yao, Yoko Tajima, Alan Packer, Robert B. Darnell & Olga G. Troyanskaya* | [Nature Genetics](https://www.nature.com/articles/s41588-019-0420-0) 28 | - **2020-02** | A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns | *Wei Jiao, Gurnit Atwal, Paz Polak, Rosa Karlic, Edwin Cuppen, PCAWG Tumor Subtypes and Clinical Translation Working Group, Alexandra Danyi, Jeroen de Ridder, Carla van Herpen, Martijn P. Lolkema, Neeltje Steeghs, Gad Getz, Quaid Morris, Lincoln D. Stein & PCAWG Consortium* | [Nature Communications](https://www.nature.com/articles/s41467-019-13825-8) 29 | 30 | 34. Tan, J., Hammond, J. H., Hogan, D. A. & Greene, C. S. ADAGE-based integration of publicly available Pseudomonas aeruginosa gene expression data with denoising autoencoders illuminates microbe-host interactions. mSystems 1, e00025-15 (2016). 31 | 35. Chen, Y., Li, Y., Narayan, R., Subramanian, A. & Xie, X. Gene expression inference with deep learning. Bioinformatics 32, 1832–1839 (2016). 32 | 36. Chen, L., Cai, C., Chen, V. & Lu, X. Learning a hierarchical representation of the yeast transcriptomic machinery using an autoencoder model. BMC Bioinformatics 17 (Suppl. 1), 9 (2016). 33 | 38. Xie, R., Wen, J., Quitadamo, A., Cheng, J. & Shi, X. A deep auto-encoder model for gene expression prediction. BMC Genomics 18 (Suppl. 9), 845 (2017). 34 | 39. Jha, A., Gazzara, M. R. & Barash, Y. Integrative deep models for alternative splicing. Bioinformatics 33, i274–i282 (2017). 35 | 40. Tripathi, R., Patel, S., Kumari, V., Chakraborty, P. & Varadwaj, P. K. DeepLNC, a long non-coding RNA prediction tool using deep neural network. Netw. Model. Anal. Health Inform. Bioinform. 5, 21 (2016). 36 | 41. Yu, N., Yu, Z. & Pan, Y. A deep learning method for lincRNA detection using auto-encoder algorithm. BMC Bioinformatics 18 (Suppl. 15), 511 (2017). 37 | 42. Hill, S. T. et al. A deep recurrent neural network discovers complex biological rules to decipher RNA protein-coding potential. Nucleic Acids Res. 46, 8105–8113 (2018). 38 | 44. Shaham, U. et al. Removal of batch effects using distribution-matching residual networks. Bioinformatics 33, 2539–2546 (2017). 39 | 45. Lin, C., Jain, S., Kim, H. & Bar-Joseph, Z. Using neural networks for reducing the dimensions of single-cell RNA-Seq data. Nucleic Acids Res. 45, e156 (2017). 40 | 50. Boža, V., Brejová, B. & Vinař, T. DeepNano: deep recurrent neural networks for base calling in MinION nanopore reads. PLoS One 12, e0178751 (2017). 41 | 54. Korvigo, I., Afanasyev, A., Romashchenko, N. & Skoblov, M. Generalising better: applying deep learning to integrate deleteriousness prediction scores for whole-exome SNV studies. PLoS One 13, e0192829 (2018). 42 | 55. Yuan, Y. et al. DeepGene: an advanced cancer type classifier based on deep learning and somatic point mutations. BMC Bioinformatics 17, 476 (2016). 43 | 56. Yousefi, S. et al. Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models. Sci. Rep. 7, 11707 (2017). 44 | 45 | ### Reviews and Perspectives 46 | #### General 47 | - **2015-05** | Deep Learning | *Yann LeCun, Yoshua Bengio & Geoffrey Hinton* | [Nature](https://www.nature.com/articles/nature14539) 48 | - **2016-07** | Deep learning for computational biology | *Christof Angermueller, Tanel Pärnamaa, Leopold Parts, Oliver Stegle* | [Molecular Systems Biology](https://www.embopress.org/doi/full/10.15252/msb.20156651) 49 | - **2017-01** | Deep learning for health informatics | *Daniele Ravì, Charence Wong, Fani Deligianni, Melissa Berthelot, Javier Andreu-Perez, Benny Lo, and Guang-Zhong Yang* | [IEEE journal of biomedical and health informatics](https://ieeexplore.ieee.org/abstract/document/7801947) 50 | - **2017-12** | Deep learning for health informatics: Recent trends and future directions | *Srivastava, Siddharth, Sumit Soman, Astha Rai, and Praveen K. Srivastava* | [2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI)](https://ieeexplore.ieee.org/abstract/document/8126082) 51 | - **2018-04** | Opportunities and obstacles for deep learning in biology and medicine | *Travers Ching, Daniel S. Himmelstein, Brett K. Beaulieu-Jones, Alexandr A. Kalinin, Brian T. Do, Gregory P. Way, Enrico Ferrero, Paul-Michael Agapow, Michael Zietz, Michael M. Hoffman, Wei Xie, Gail L. Rosen, Benjamin J. Lengerich, Johnny Israeli, Jack Lanchantin, Stephen Woloszynek, Anne E. Carpenter, Avanti Shrikumar, Jinbo Xu, Evan M. Cofer, Christopher A. Lavender, Srinivas C. Turaga, Amr M. Alexandari, Zhiyong Lu, David J. Harris, Dave DeCaprio, Yanjun Qi, Anshul Kundaje, Yifan Peng, Laura K. Wiley, Marwin H. S. Segler, Simina M. Boca, S. Joshua Swamidass, Austin Huang, Anthony Gitter and Casey S. Greene* | [Journal of The Royal Society Interface](https://royalsocietypublishing.org/doi/full/10.1098/rsif.2017.0387) 52 | - **2018-06** | Next-Generation Machine Learning for Biological Networks | *Diogo M.Camacho, Katherine M.Collins, Rani K.Powers, James C.Costello, James J.Collins* | [Cell](https://www.sciencedirect.com/science/article/pii/S0092867418305920) 53 | - **2018-09** | Deep learning in biomedicine | *Michael Wainberg, Daniele Merico, Andrew Delong & Brendan J Frey* | [Nature Biotechnology](https://www.nature.com/articles/nbt.4233) 54 | - **2019-01** | A guide to deep learning in healthcare | *Andre Esteva, Alexandre Robicquet, Bharath Ramsundar, Volodymyr Kuleshov, Mark DePristo, Katherine Chou, Claire Cui, Greg Corrado, Sebastian Thrun & Jeff Dean* | [Nature Medicine](https://www.nature.com/articles/s41591-018-0316-z) 55 | - **2019-01** | Guidelines for reinforcement learning in healthcare | *Omer Gottesman, Fredrik Johansson, Matthieu Komorowski, Aldo Faisal, David Sontag, Finale Doshi-Velez & Leo Anthony Celi* | [Nature Medicine](https://www.nature.com/articles/s41591-018-0310-5) 56 | - **2019-03** | Reinforcement learning in artificial and biological systems | *Emre O. Neftci and Bruno B. Averbeck* | [Nature Machine Intelligence](https://www.nature.com/articles/s42256-019-0025-4.pdf?origin=ppub) 57 | 58 | #### Genomics 59 | - **2016-01** | Machine learning in genomic medicine: a review of computational problems and data sets | *Michael KK Leung, Andrew Delong, Babak Alipanahi, and Brendan J Frey* | [Proceedings of the IEEE](https://ieeexplore.ieee.org/abstract/document/7347331) 60 | - **2018-05** | Deep learning of genomic variation and regulatory network data | *Amalio Telenti, Christoph Lippert, Pi-Chuan Chang, Mark DePristo* | [Human Molecular Genetics](https://academic.oup.com/hmg/article-abstract/27/Supplement_R1/R63/4966854) 61 | - **2018-05** | Deep Learning for Genomics: A Concise Overview | *Tianwei Yue, Haohan Wang* | [Invited chapter for Springer Book: Handbook of Deep Learning Applications](https://arxiv.org/abs/1802.00810) 62 | - **2019-01** | A primer on deep learning in genomics | *James Zou, Mikael Huss, Abubakar Abid, Pejman Mohammadi, Ali Torkamani and Amalio Telenti* | [Nature Genetics](https://www.nature.com/articles/s41588-018-0295-5) | [Google Colab Notebook with tutorial](https://colab.research.google.com/drive/17E4h5aAOioh5DiTo7MZg4hpL6Z_0FyWr) 63 | - **2019-04** | Deep learning: new computational modelling techniques for genomics| *Gökcen Eraslan, Žiga Avsec, Julien Gagneur & Fabian J. Theis* | [Nature Reviews Genetics](https://www.nature.com/articles/s41576-019-0122-6) 64 | --------------------------------------------------------------------------------