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22 | ## A L4 innovative AGI System Empowering miRNA Drug Discovery
23 |
24 | ## 1. Introduction
25 |
26 | In the field of life and health sciences, the DeepWism® platform has recently demonstrated exceptional capabilities in scientific hypothesis generation. Aging is a multifaceted biological process governed by complex molecular pathways, posing significant challenges for the development of effective therapeutic interventions.
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28 | In a joint study conducted by **DeepWism Intelligence, Allife Medicine Co., Ltd, the University of Cambridge**, and the **Wellcome Sanger Institute**, we introduced an AI-powered platform that integrates **semantic indexing, biomedical knowledge graphs**, and the proprietary **DeepRanking algorithm** to identify miRNAs with potential anti-aging effects—marking a major breakthrough in gene-based therapeutic discovery.
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30 | A joint manuscript is currently being prepared by the collaborating institutions for submission to a peer-reviewed journal, aiming to contribute these findings to the ongoing scientific discourse on aging and RNA therapeutics.
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32 | The platform generated **100 scientific hypotheses**, which were subjected to multiple rounds of wet-lab validation, achieving a **71% success rate**—a substantial improvement over the ~10% success rate typically seen with human expert approaches. Notably, one of the validated hypotheses involved **exosome-mediated delivery** of a specific miRNA, which was shown to significantly suppress cellular senescence and promote collagen remodeling. This candidate has now advanced to clinical trials, establishing a new paradigm for AI-driven miRNA therapeutics.
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34 | At its core, **DeepWism® R2** is built upon the **Thin-Thick-Thin Crowd Entropy Dynamics System (T3CEDS)** framework, which introduces entropy reduction as the key mechanism for intelligent reasoning. This enables the transformation of miRNA research from trial-and-error under high entropy, into a closed-loop of precise hypothesis generation and validation.
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36 | ### The architecture includes three entropy-reducing layers:
37 |
38 | - **Thin Perception Layer**
39 | Efficiently captures and encodes multi-modal inputs (e.g., publications, patents, omics data), significantly reducing input entropy.
40 |
41 | - **Thick Processing Layer**
42 | Uses Crowd intelligence to perform structured reasoning and deep analysis, systematically lowering entropy while uncovering high-potential therapeutic candidates.
43 |
44 | - **Thin Decision Layer**
45 | Condenses complex reasoning outputs into high-confidence hypotheses and actionable experimental plans.
46 |
47 | ---
48 |
49 | ## 2. Key Features
50 |
51 | ### Entropy-Driven Design
52 | Unlike traditional attention-based AI models, DeepWism® R2 is built around entropy management. This design enhances data integration, hypothesis generation, and interpretability—addressing complexity and uncertainty in biomedical research.
53 |
54 | ### Crowd Intelligence Processing
55 | The Thick Processing Layer applies collaborative reasoning across multi-source data, enabling the accurate identification and prioritization of novel miRNA targets under high-entropy constraints.
56 |
57 | ### Broad Applicability & Scalability
58 | DeepWism® R2 is applicable across various domains such as anti-aging medicine, oncology, metabolic disorders, and neurological diseases—empowering biotech teams to rapidly expand their discovery pipelines.
59 |
60 | ---
61 |
62 | ## 3. Experimental Validation & Real-World Application
63 |
64 | **DeepWism® R2** has been successfully deployed in collaborative research projects with leading medical institutions, validating its effectiveness in miRNA drug discovery.
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69 |
70 | **Figure 1: Knowledge-Integrated miRNA Candidate Identification Architecture**
71 |
72 | DeepWism® R2 integrates literature sources and biomedical databases to build a semantic index and knowledge graph. Through literature retrieval, entity ranking (DeepRanking), and filtering modules, it intelligently identifies and prioritizes miRNA candidates associated with aging.
73 |
74 | ### Case Study: Anti-Aging & Regenerative Medicine
75 |
76 | #### miRNA Target Discovery
77 | DeepWism® R2 accurately identified key miRNA targets, which were experimentally validated via exosome delivery in both cellular and animal models—demonstrating effects such as wrinkle reduction, collagen remodeling, and p16/p53 downregulation.
78 |
79 | #### Clinical Translation
80 | The candidates advanced quickly to clinical trials, where early results showed significant skin rejuvenation and anti-aging effects—setting a new standard for AI + miRNA-based therapeutic innovation.
81 |
82 | ---
83 |
84 | ## 4. Chat Website
85 |
86 | DeepWism® is now offering free advanced research services to all global research institutions and innovative drug development companies.
87 | To request access, please email **r2@deepwism.com** or visit **[i.deepwism.com](https://i.deepwism.com)** to obtain a DeepWism invitation code.
88 |
89 | **Experience DeepWism® R2's revolutionary capabilities through our interactive platforms:**
90 |
91 | - **Chat Interface:** [i.deepwism.com](https://i.deepwism.com)
92 |
93 | ---
94 |
95 | ## 5. Contact
96 | For questions, collaborations, or support ，please contact us at: r2@deepwism.com
97 |
98 | Website: www.deepwism.com
99 |
100 | GitHub Issues: Report bugs or request features
101 | Twitter: @DeepWism
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Advancing Next Generation AI through Entropy Reduction and Crowd Intelligence
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