hussius/deeplearning-biology

What it solves

This project addresses the challenge of navigating the vast and rapidly growing landscape of deep learning applications in biology. It provides a curated, community-driven directory of implementations, research papers, and resources to help researchers find existing tools and models for tasks ranging from genomics and protein structure prediction to drug discovery.

How it works

The repository acts as a structured index. It categorizes various deep learning methods—such as convolutional neural networks, transformers, and language models—by their specific biological application. Each entry typically links to the original research paper, the source code repository, and sometimes additional resources like tutorials or pre-trained models on platforms like HuggingFace or TensorFlow Hub.

Who it’s for

This list is designed for researchers, bioinformaticians, and data scientists who are either looking to apply deep learning to biological problems or want to stay updated on the latest computational methods in the field. It is particularly useful for those working in genomics, proteomics, and systems biology.

Highlights

• Organised directory of deep learning implementations across diverse biological subfields.
• Focuses on practical resources like model zoos (e.g., Kipoi) and tutorials for getting started.
• Includes a dedicated section for review papers to help users understand the conceptual foundations of the field.
• Highlights state-of-the-art sequence modelling techniques, including protein and genomic language models.
• Encourages community contributions to keep the list current and comprehensive.