deeplearning-biology
A curated list of deep learning implementations and resources for biological research, with a focus on genomics.
What is deeplearning-biology?
deeplearning-biology is a curated, community-maintained list of deep learning implementations, tools, and resources specifically for biological research. It aggregates code repositories, model zoos, and key papers to help researchers and developers apply state-of-the-art ML techniques to problems in genomics, protein biology, drug discovery, and more. The project solves the problem of fragmented information by providing a centralized, categorized reference.
Bioinformaticians, computational biologists, and machine learning researchers or engineers who want to apply or understand deep learning in biological contexts. It's especially useful for those entering the field or looking for practical implementations beyond theoretical papers.
Unlike generic ML resource lists, it is domain-specific, implementation-focused, and community-driven. It saves significant time by filtering for biological relevance and code availability, and it emphasizes real-world tools over purely academic descriptions.
Overview
A list of deep learning implementations in biology
Use Cases
Best For
- Finding ready-to-use deep learning code for genomics tasks like variant calling or enhancer prediction
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