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A list of deep learning implementations in biology
| Date | Stars |
|---|---|
| 2026-07-31 | 2152 |
| 2026-08-02 | 2152 |
| 2026-08-06 | 2155 |
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# deeplearning-biology
This is a list of implementations of deep learning methods to biology, originally published on [Follow the Data](https://followthedata.wordpress.com/). There is a slant towards genomics because that's the subfield that I follow most closely.
Please, contribute to this growing list, especially in categories that I haven't covered well!
You might also want to refer to the [awesome deepbio](https://github.com/gokceneraslan/awesome-deepbio) list.
## Table of contents
- [Reviews](#reviews)
- [Model repositories and resources](#repositories)
- [Sequence modelling](#seqmodels)
- [Multi-omics integration](#integration)
- [Protein biology](#protein_biology)
- [Structure prediction](#protein_biology_structure_prediction)
- [Protein design](#protein_biology_design)
- [Function prediction](#protein_biology_function_prediction)
- [Genomics](#genomics)
- [Variant calling](#genomics_variant-calling)
- [Gene expression](#genomics_expression)
- [Imaging and gene expression](#imaging_expression)
- [Predicting enhancers and regulatory regions](#genomics_enhancers)
- [Non-coding RNA](#genomics_non-coding)
- [Methylation](#genomics_methylation)
- [Single-cell applications](#genomics_single-cell)
- [Chemoinformatics and drug discovery](#chemo)
- [Biomarker discovery](#biomarker)
- [Metabolomics](#metabolomics)
- [Generative models](#generative)
- [Population genetics](#genomics_pop)
- [Systems biology](#sysbio)
## Reviews <a name="reviews"></a>
These are not implementations as such, but contain useful pointers. Because review papers in this field are more time-sensitive, I have added the month of journal publication. Note that the original preprint may in some cases have been available online long before the published version.
**(2021-11) A Unified View of Relational Deep Learning for Polypharmacy Side Effect, Combination Synergy, and Drug-Drug Interaction Prediction** [[open access paper](https://arxiv.org/pdf/2111.02916v1.pdf)]
In recent years, numerous machine learning models which attempt to solve polypharmacy side effect identification, drug-drug interaction prediction and combination therapy design tasks have been proposed. Here, we present a unified theoretical view of relational machine learning models which can address these tasks. We provide fundamental definitions, compare existing model architectures and discuss performance metrics, datasets and evaluation protocols. In addition, we emphasize possible high impact applications and important future research directions in this domain.
**(2019-12) Deep learning of pharmacogenomics resources: moving towards precision oncology** [[Briefings in Bioinformatics](https://academic.oup.com/bib/advance-article/doi/10.1093/bib/bbz144/5669856#186956080)]
**(2019-04) Deep learning: new computational modelling techniques for genomics** [[Nature Reviews Genetics paper](https://www.nature.com/articles/s41576-019-0122-6)]
This is a very nice conceptual review of how deep learning can be used in genomics. It explains how convolutional networks, recurrent networks, graph convolutional networks, autoencoders and GANs work. It also explains useful concepts like multi-modal learning, transfer learning, and model explainability.
**(2019-01) A guide to deep learning in healthcare** [[Nature Medicine paper](https://www.nature.com/articles/s41591-018-0316-z)]
From the abstract: "Here we present deep-learning techniques for healthcare, centering our discussion on deep learning in computer vision, natural language processing, reinforcement learning, and generalized methods. We describe how these computational techniques can impact a few key areas of medicine and explore how to build end-to-end systems. Our discussion of computer vision focuses largely on medical imaging, and we describe the application of natural language processing to domains such as electronic health record data. Similarly, reinforcement learning isExcerpt of 70,655 characters
Read on GitHub
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hsiao yi
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Jérémie Kalfon · BroadInstitute - PiPle - PasteurInstitute
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Z · Shanghai Jiao Tong University
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Justin Shenk · Germany
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Caffery Yang · Texas A&M University
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Benedek Rozemberczki · @google · United Kingdom
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Joseph Paul Cohen PhD
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:ed1872b9fda2c247, desc:a list of