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Computational Biology

Awesome Computational Biology Awesome

A curated collection of databases, software, and papers related to computational biology.

Computational biology involves the development and application of data-analytical and theoretical methods, mathematical modelling and computational simulation techniques to the study of biological, ecological, behavioural, and social systems. — Wikipedia

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Databases

scRNA

Compound

  • PubChem — One of the largest chemical databases (compounds, genes, and proteins).
  • ChEBI — Database focused on small chemical compounds.
  • ChEMBL — Bioactive molecules with drug-like properties.
  • ChemSpider — Chemical structure database.
  • DrugTargetCommons — Community platform for curating and integrating experimental bioactivity data across drugs and targets.
  • HMDB (Human Metabolome Database) — Comprehensive database of small molecule metabolites found in the human body.
  • KEGG COMPOUND — Collection of small molecules and biopolymers.
  • LIPID MAPS — Database of lipids.
  • Rhea — Database of chemical reactions.
  • DrugCentral — Online drug compendium with drug mode of action and indication information.
  • Drug Repurposing Hub — Collections of drug repurposing data (drug, MoA, target, etc).
  • Therapeutic Target Database — Drug-target, target-disease, and drug-disease datasets.
  • ZINC ligand discovery database — Free database of commercially-available compounds for virtual screening.

Pathway

  • PathwayCommons — Database of pathways and interactions.
  • KEGG PATHWAY — Collection of pathway maps.
  • WikiPathways — Database of biological pathways.
  • Reactome — Expert-curated, peer-reviewed pathway database with detailed reaction mechanisms.
  • BioCyc — Collection of pathway/genome databases across thousands of organisms.
  • SIGNOR — Database of causal signaling interactions and pathways.
  • MSigDB (Molecular Signatures Database) — Curated gene sets derived from pathways and biological processes.

Mass Spectra

  • MassBank — Open source databases and tools for mass spectrometry reference spectra.
  • MoNA MassBank of North America — Meta-database of metabolite mass spectra, metadata, and associated compounds.

Protein

  • THE HUMAN PROTEIN ATLAS — Comprehensive human protein database (cells, tissues, organs).
  • PROTEIN DATA BANK (PDB) — 3D structures of proteins, nucleic acids, complexes.
  • UniProt — Functional information on proteins.
  • AlphaFold Protein Structure Database — 3D protein structure predictions.
  • RCSB Protein Data Bank — Repository for structural data of biological molecules.
  • Critical Assessment of Structure Prediction (CASP) — Assessing methods for protein structure prediction.
  • Uniclust — Clustered protein sequence databases.
  • UniRef — Non-redundant sequence database clustering UniProtKB entries at multiple sequence identity thresholds.
  • CATH database — Hierarchical classification of protein domain structures.
  • SAbDab — Structural Antibody Database containing all antibody structures in the PDB.
  • OADB (Observed Antibody Space Database) — Database of antibody sequences from immune repertoire sequencing.
  • InterPro — Protein families, domains, and functional sites database integrating 14 member databases including Pfam and PROSITE.
  • Pfam — Database of protein families described by multiple sequence alignments and hidden Markov models.
  • NeXtProt — Expert knowledge base on human proteins with deep functional annotation, complementary to UniProt.

Genome

  • ENCODE — Encyclopedia of DNA Elements; regulatory and functional genomic elements across the genome.
  • Ensembl — Genome browser and annotation database for vertebrate and other eukaryotic genomes.
  • Human Genome Resources at NCBI — Database for genomics, proteomics, transcriptomics, and systems biology.
  • GenBank — NCBI's database of genetic sequences.
  • UCSC Genome Browser — UCSC's genome browser.
  • cBioPortal — Cancer genomics database; aggregating many patient datasets.
  • 10x Genomics Dataset — Collection of single-cell datasets.
  • The Genotype-Tissue Expression (GTEx) — Human gene expression and regulation resource.
  • Dependency Map (DepMap) — CRISPR-Cas9 screens in cancer cell lines.
  • Catalogue Of Somatic Mutations In Cancer (COSMIC) — Resource on somatic mutations in cancers.
  • MGnify — Resource for metagenomic and metatranscriptomic data.
  • JASPAR — Database of transcription factor binding profiles.
  • gnomAD — Genome Aggregation Database; genetic variation from large-scale sequencing projects.
  • Rfam — Database of RNA families with sequence alignments and consensus structures.
  • ROADMAP Epigenomics — Reference epigenome maps for 111 primary human cell types and tissues, including histone modifications, chromatin accessibility, and DNA methylation.
  • FANTOM5 — Functional annotation of mammalian genome; comprehensive atlas of active enhancers, promoters, and transcription start sites across human and mouse cell types.

Disease

  • KEGG DRUG — Comprehensive, approved drug information.
  • DrugBank — Database of drugs and targets (University of Alberta).
  • DisGeNET — Database of gene-disease associations integrating expert-curated and GWAS data.
  • OMIM (Online Mendelian Inheritance in Man) — Comprehensive database of human genes and genetic disorders.
  • Open Targets Platform — Systematic target identification and prioritization platform integrating genetics, genomics, and drug data for drug discovery.
  • Human Phenotype Ontology (HPO) — Standardized vocabulary of phenotypic abnormalities in human disease, linking genes, variants, and clinical features.
  • DISEASES — Gene–disease association database integrating evidence from text mining, curated databases, and experimental data.

Interaction

Drug-Gene Interaction

  • DGIdb — Drug-gene interactions and the druggable genome.
  • Comparative Toxicogenomics Database — Chemical-gene interactions, chemical-disease and gene-disease associations, chemical-phenotype associations.
  • SNAP — Dataset of drug-gene interactions.

Drug (Cell Line) Response

Chemical-Protein Interaction

Protein-Protein Interaction

  • STRING — PPI networks for multiple organisms.
  • BioGRID — Protein, genetic, and chemical interactions.
  • HIPPIE — Human protein-protein interaction database.
  • IntAct — Open-source molecular interaction database and analysis system from EMBL-EBI.

Knowledge Graph

  • Drug Mechanism Database (DrugMechDB) — Mechanisms of action from drug to disease.
  • DRKG — Large-scale biological knowledge graph for drug discovery.
  • Hetionet — Heterogeneous network integrating genes, diseases, drugs, pathways, and more.
  • PrimeKG — Multi-modal precision medicine knowledge graph integrating clinical, genetic, and drug data.

Gene Regulatory Network

  • TRRUST — Manually curated database of human and mouse transcriptional regulatory interactions between transcription factors and their target genes.
  • RegNetwork — Database of gene regulatory networks covering transcription factor–target gene and miRNA–gene interaction data across multiple species.
  • miRBase — Reference repository for microRNA gene annotations, sequences, and experimentally validated targets.

Clinical Trial

Benchmarks & Datasets

  • 1000 Genomes Project — Reference panel of human genetic variation from 2,504 individuals across 26 populations.
  • BACE — Binary classification and regression dataset for β-secretase 1 (BACE-1) inhibitor binding affinity.
  • BEAT AML — Functional ex vivo drug sensitivity measurements paired with genomics for acute myeloid leukemia.
  • BindingDB Curated Sets — Curated binding affinity datasets for protein–ligand interaction benchmarking.
  • Cancer Therapeutics Response Portal (CTRP) — Drug sensitivity profiles across ~900 cancer cell lines for >400 compounds.
  • ClinTox — Clinical toxicity dataset contrasting FDA-approved drugs with those that failed clinical trials due to toxicity.
  • CPTAC (Clinical Proteomic Tumor Analysis Consortium) — Multi-omic proteogenomic datasets for multiple cancer types linking proteomics with genomics.
  • CrossDocked2020 — Large-scale dataset for structure-based virtual screening.
  • DUD-E (Directory of Useful Decoys, Enhanced) — Structure-based virtual screening benchmark with active ligands and challenging decoy sets across diverse protein targets.
  • FLIP (Fitness Landscape Inference for Proteins) — Benchmark collection of protein fitness landscape datasets for evaluating protein ML models.
  • Genomics of Drug Sensitivity in Cancer (GDSC) — Drug sensitivity for ~1000 human cancer cell lines and hundreds of compounds.
  • GuacaMol — Benchmark suite for generative molecular design models.
  • JUMP Cell Painting Datasets — Consortium-scale cell imaging perturbation datasets (chemical and genetic) for phenotypic profiling and drug discovery research.
  • LINCS L1000 — Gene expression profiles (978 landmark genes) for >20,000 chemical and genetic perturbations across cell lines.
  • MoleculeNet — Benchmark datasets for molecular machine learning.
  • MOSES — Benchmarking platform for molecular generation models.
  • NCI60 — Drug sensitivity benchmark across 60 diverse human cancer cell lines.
  • OGB (Open Graph Benchmark) — Large-scale graph ML benchmark suite including biological datasets such as ogbl-ppa (protein-protein associations) and ogbg-molhiv.
  • OpenBioLink — Benchmark datasets for biological knowledge graph completion.
  • PharmGKB — Curated pharmacogenomics dataset linking genetic variants to drug response phenotypes across thousands of drugs.
  • PK-DB — Open database of experimental pharmacokinetics (PK) and ADME data from clinical and preclinical studies.
  • PRISM — Cancer drug sensitivity profiling of >4,500 drugs across >900 cancer cell lines using pooled-cell-line barcoding.
  • ProteinGym — Large-scale benchmark of deep mutational scanning assays for evaluating protein fitness landscape models.
  • QM9 — Quantum chemistry properties for 134K stable small organic molecules computed at DFT level.
  • scIB (Single-cell Integration Benchmarks) — Comprehensive benchmarking framework for single-cell data integration methods.
  • scPerturb — Curated and continuously updated single-cell perturbation data resource spanning CRISPR and drug perturbation studies.
  • SIDER (Side Effect Resource) — Database of 1,430 approved drugs with their recorded adverse drug reactions across 27 system-organ classes.
  • Tabula Muris — Comprehensive single-cell atlas of 20 mouse organs and tissues, enabling cross-tissue and cross-species comparisons.
  • Tabula Sapiens — Comprehensive human single-cell atlas of ~500K cells from 24 organs and tissues across multiple donors.
  • TAPE (Tasks Assessing Protein Embeddings) — Benchmark suite of five biologically meaningful semi-supervised learning tasks for evaluating protein representations.
  • The Cancer Genome Atlas (TCGA) — Comprehensive multi-omics (genomics, transcriptomics, proteomics, methylation) dataset for 33 cancer types across ~11,000 patients.
  • Therapeutics Data Commons (TDC) — Unified benchmark suite covering ADMET, drug-target interaction, drug response, and more.
  • Tox21 — 12,707 compounds tested in 12 nuclear receptor and stress-response pathway biochemical assays for toxicity prediction.
  • UK Biobank — Large-scale biomedical database of ~500K participants with genetic, imaging, and health data for population genetics and disease studies.

API

Preprocessing Tools

  • Chemistry Development Kit — Cheminformatics software & machine learning tools.
  • Biopython — Collection of Python tools for biological computation including sequence analysis, structure parsing, and database access.
  • FlashDeconv — High-performance spatial transcriptomics deconvolution (~1M spots in ~3 min).
  • RDKit — Cheminformatics software & machine learning toolkit.
  • DeepChem — Deep learning library for drug discovery, quantum chemistry, and materials science.
  • ChatSpatial — MCP server for spatial transcriptomics analysis via natural language.
  • Scanpy — Python library for scRNA-seq analysis.
  • Seurat — R library for scRNA-seq analysis.
  • scvi-tools — Probabilistic models for single-cell omics data analysis.
  • CellTypist — Automated cell type annotation for scRNA-seq.
  • Squidpy — Python library for spatial single-cell analysis.
  • GROMACS — Molecular dynamics simulation package for biochemical molecules.
  • MDAnalysis — Python library for analyzing and altering molecular dynamics simulation trajectories.
  • OpenMM — High-performance toolkit for molecular simulation and GPU-accelerated MD.
  • scVelo — RNA velocity estimation for single-cell transcriptomics, inferring the direction and speed of cell differentiation.
  • STAR — Ultrafast universal RNA-seq aligner with support for spliced alignment and single-cell quantification via STARsolo.
  • kallisto — Near-optimal RNA-seq quantification using pseudoalignment for fast transcript abundance estimation.
  • Harmony — Fast and scalable integration of single-cell data across datasets, conditions, technologies, and species.
  • Monocle3 — Single-cell trajectory analysis tool for learning developmental trajectories and ordering cells in pseudotime.
  • CellChat — Inference and analysis of cell-cell communication ligand-receptor networks from single-cell transcriptomics data.
  • SCENIC — Single-cell regulatory network inference and clustering linking transcription factors to co-expressed gene modules.
  • DoubletFinder — Machine learning approach for detecting multiplet (doublet) artifacts in single-cell RNA-seq data.
  • Numbat — Haplotype-aware copy number variation inference from single-cell RNA-seq using hidden Markov models.
  • CaSpER — CNV identification and visualization by integrative analysis of single-cell or bulk RNA-seq data.
  • CellCharter — Identification and characterization of spatial cell niches from spatial transcriptomics using VAEs and Gaussian mixture models.
  • STAGATE — Adaptive graph attention auto-encoder for spatial domain identification in spatial transcriptomics.
  • NCEM — GNN-based model for learning intercellular communication from spatial graphs of cells.
  • DeepTalk — Graph attention network for deciphering cell-cell communication from spatial transcriptomics data.
  • COMMOT — Optimal transport-based framework for screening cell-cell communication in spatial transcriptomics.
  • TIGON — Neural optimal transport method for reconstructing growth and dynamic trajectories from single-cell transcriptomics.
  • LINGER — Neural network for gene regulatory network inference from single-cell multiome (RNA+ATAC-seq) data with bulk data pretraining.
  • sciPENN — RNN-based method for simultaneous protein expression prediction, uncertainty estimation, and cell-type label transfer from CITE-seq and scRNA-seq data.
  • MOGONET — Multi-omics graph convolutional network framework for patient classification and biomarker identification.

Machine Learning Tasks and Models

Drug Discovery

Drug Response Prediction

  • drGAT — Attention-based model for drug response prediction with gene explainability.
  • MOFGCN — GCN + heterogeneous network.
  • DeepDSC — Autoencoder + fully connected NN.
  • DGDRP — Multi-view embedding neural network.
  • DeepAEG — GNN embedding + attention mechanism.
  • RECOVER — Machine learning framework for predicting synergistic drug combination responses across cell lines.
  • TGSA — Tumor gene set and attention-based model leveraging biological pathway knowledge for drug response prediction.
  • HiDRA — Hierarchical network model incorporating gene and pathway-level information for cancer drug response prediction.
  • PRNet — Deep generative model for predicting transcriptional responses to novel chemical perturbations for drug discovery.
  • chemCPA — Compositional perturbation autoencoder for predicting single-cell transcriptional responses to unseen drug perturbations and dose combinations.
  • cycleCDR — Interpretable cycle-consistency framework for modeling cellular responses to drug perturbations.
  • DRUML — Ensemble machine learning framework combining standard ML with deep learning to systematically rank anti-cancer drugs from proteomics and RNA-seq data.

Drug Repurposing

  • DeepPurpose — Deep learning library for drug repurposing.
  • TranSiGen — Dual-VAE architecture for ligand-based virtual screening, drug response prediction, and drug repurposing using chemical-induced transcriptional profiles.

Drug Target Interaction

  • NeoDTI — Library for drug-target interaction prediction.
  • DTINet — Network-based framework integrating heterogeneous biological data for DTI prediction.
  • DeepDTA — Deep learning model using CNNs on protein sequences and drug SMILES.
  • GraphDTA — Graph neural network–based DTI prediction using molecular graphs.
  • MolTrans — Transformer-based DTI model leveraging molecular substructures.
  • DrugBAN — Bilinear attention network for interpretable DTI prediction.

Compound-Protein Interaction

  • MCPINN — Drug discovery via compound-protein interaction and machine learning.
  • TransformerCPI — CPI prediction using Transformer.

Molecular Generation

  • REINVENT — Reinforcement learning for de novo drug design.
  • MolGPT — Transformer-based model for molecular generation.
  • Molecular Transformer — Sequence-to-sequence model for retrosynthesis prediction.
  • TargetDiff — 3D equivariant diffusion model for structure-based drug design.
  • DiffDock — Diffusion generative model for molecular docking, predicting the binding pose of small molecules to protein targets.
  • JTVAE — Junction tree variational autoencoder for molecular graph generation that guarantees chemical validity via a hierarchical tree decomposition.
  • DiffSBDD — Equivariant diffusion model for structure-based drug design that generates molecules and binding conformations for protein targets.
  • ReLeaSE — Deep reinforcement learning framework for de novo drug design combining a generative and predictive model.
  • PaccMannRL — Reinforcement learning-based generative model for de novo hit-like anticancer molecule design from transcriptomic data.

LLM for Biology

  • AI4Chem/ChemLLM-7B-Chat — LLM for chemical & molecular science.
  • BioGPT — LLM for biomedical text generation.
  • GeneGPT — LLM for biomedical information, integrated with various APIs.
  • GenePT — Foundation LLM for single-cell data.
  • scPRINT — Pretrained on 50M cells for scRNA-seq denoising & zero imputation.
  • ClawBio — Bioinformatics-native AI agent skill library with local-first pharmacogenomics, ancestry PCA, semantic similarity, nutrigenomics, and metagenomics skills.
  • BioMedLM — 2.7B parameter GPT-2-style language model trained exclusively on biomedical literature from PubMed for biomedical question answering and text generation.
  • MolT5 — Language model for molecular tasks bridging text and SMILES, enabling molecule captioning and text-driven molecule generation.
  • ChatDrug — LLM-based conversational pipeline for drug discovery, using natural language prompts for iterative drug editing and optimization.

Foundation Models

Single-cell Foundation Models

Transcriptomics Foundation Models
  • scFoundation — Large-scale foundation model for single-cell gene expression, enabling multiple downstream tasks.
  • scGPT — Transformer-based foundation model pretrained on millions of single-cell profiles.
  • Geneformer — Context-aware, attention-based deep learning model pretrained on a large corpus of single-cell transcriptomes.
  • BulkFormer — Foundation model for bulk RNA-seq data; learns general transcriptomic representations.
  • scBERT — BERT-based foundation model pretrained on large-scale scRNA-seq data for cell type annotation.
  • CellPLM — Cell pre-trained language model with inter-cell transformer architecture for diverse single-cell analysis tasks.
  • UCE — Universal Cell Embeddings: zero-shot single-cell embedding model trained on 36M cells across species, tissues, and assays without fine-tuning.
  • GEARS — Graph-based model for predicting transcriptional responses to single and combinatorial genetic perturbations using biological priors.
  • SATURN — Transformer-based model integrating gene expression and protein sequences via a protein language model to learn unified multi-species cell embeddings.
  • CancerFoundation — Single-cell RNA-seq foundation model trained exclusively on a curated dataset of malignant cells to learn cancer-specific embeddings.
Spatial Foundation Models
  • GigaPath — Slide-level digital pathology foundation model pretrained on 1.3 billion pathology image tokens from whole-slide images.
  • UNI — General-purpose self-supervised pathology foundation model trained on 100K+ whole-slide images for diverse computational pathology tasks.
  • CONCH — Vision-language foundation model for computational pathology trained with contrastive captioning on pathology image–text pairs.
  • Phikon — ViT-based pathology foundation model pretrained with iBOT self-supervision on TCGA whole-slide images.
  • Nicheformer — Foundation model for single-cell and spatial omics using a transformer architecture with positional embeddings to encode spatial cell information.
  • scGPT-spatial — Extension of scGPT for spatial transcriptomics with continual pretraining and a mixture-of-experts decoder for spatial gene expression analysis.
Multi-Omics Foundation Models
  • scMulan — Single-cell multi-omic language model pretrained on ~10M cells spanning transcriptomics, epigenomics, and proteomics for cross-omics transfer tasks.
  • totalVI — Probabilistic framework for joint analysis of paired scRNA-seq and protein (CITE-seq) data enabling multi-modal cell state representation across single-cell datasets.
  • MultiVI — Multi-modal variational autoencoder for integrating paired and unpaired single-cell RNA-seq and ATAC-seq measurements into a unified latent space.
  • MIRA — Probabilistic multimodal topic model jointly modeling single-cell transcriptomics and chromatin accessibility for regulatory network inference.
  • GLUE — Graph-Linked Unified Embedding framework for unpaired single-cell multi-omics data integration across RNA, ATAC, methylation, and protein modalities.
  • BABEL — Cross-modality translation model enabling prediction between scRNA-seq and scATAC-seq profiles without requiring paired single-cell measurements.
  • Multigrate — Asymmetric multi-omics variational autoencoder for integrating single-cell data across RNA, ATAC, and protein modalities with missing-modality support.
  • MOFA+ — Multi-Omics Factor Analysis framework identifying shared axes of variation across bulk and single-cell datasets including RNA, ATAC, proteomics, methylation, and copy number.
  • GeneCompass — Large-scale foundation model integrating DNA regulatory sequences and single-cell transcriptomics from 120M+ cells across multiple species for gene regulation prediction.
  • UnitedNet — Interpretable multi-task deep neural network for single-cell multi-omics integration spanning transcriptomics, chromatin accessibility, and proteomics.
  • SpatialGlue — Graph attention network for spatial multi-omics integration jointly embedding spatial transcriptomics with chromatin accessibility or proteomics.
  • MIDAS — Mosaic integration and differential accessibility model for single-cell multi-omics data that handles arbitrary missing-modality combinations across transcriptomics, chromatin accessibility, and proteomics.
  • Concerto — Contrastive self-supervised learning framework for single-cell multimodal data integration, batch correction, and reference-query mapping.
  • scButterfly — Dual-aligned variational autoencoder for single-cell cross-modality translation between paired and unpaired multiomics data.
  • JAMIE — Joint variational autoencoder for multimodal single-cell data imputation and embedding.
  • scPair — Bidirectional feedforward network for single-cell multimodal analysis with cross-modality prediction leveraging single-cell atlases.
Domain Alignment
  • scArches — Transfer learning framework for mapping new single-cell datasets onto pre-trained reference atlases across batches, conditions, and modalities.
  • TOSICA — Transformer-based framework for one-stop interpretable cell-type annotation supporting cross-dataset and cross-species transfer.

Protein Foundation Models

Pre-trained Embedding
  • Evolutionary Scale Modeling (ESM) — Protein embeddings.
  • ChemBERTa-2 — Chemical embeddings & prediction.
  • ProtTrans — Suite of protein language models (ProtBERT, ProtT5, ProtXLNet) trained on billions of protein sequences from UniRef and BFD.
  • ProGen2 — Protein language model trained on diverse protein families for sequence generation and fitness prediction.
  • Ankh — Efficient protein language model optimized for downstream prediction tasks including secondary structure, localization, and function annotation.
Protein Structure Prediction and Design
  • AlphaFold3 — Predicts structures of proteins, nucleic acids, small molecules, and their complexes.
  • Boltz-1 — Open-source all-atom biomolecular structure prediction model for proteins, nucleic acids, small molecules, and their complexes achieving AlphaFold3-level accuracy.
  • Chai-1 — Unified molecular structure prediction model covering proteins, nucleic acids, small molecules, and complexes.
  • ESM3 — Multimodal protein language model that jointly reasons over sequence, structure, and function for generative protein design and engineering.
  • ESMFold — Fast protein structure prediction using language model embeddings.
  • RFdiffusion — Generative model for protein backbone design using diffusion.
  • ProteinMPNN — Deep learning model for protein sequence design given backbone structure.
  • OmegaFold — High-resolution de novo protein structure prediction from sequence.
  • RoseTTAFold — Three-track neural network for protein structure prediction.
  • OpenFold — Trainable, memory-efficient open-source reproduction of AlphaFold2 enabling custom protein structure prediction workflows.
  • SaProt — Structure-aware protein language model using structure-aware tokens that encode both sequence and backbone geometry for improved function prediction.
  • EvoDiff — Discrete diffusion framework for protein sequence generation trained on evolutionary-scale data, supporting unconditional generation, disordered region design, and functional motif scaffolding. [ paper-2023 ]

Multi-Modal Foundation Models

  • CHIEF — Clinical Histopathology Imaging Evaluation Foundation model integrating histology images and clinical context for pan-cancer analysis.
  • BiomedCLIP — CLIP-based vision-language foundation model for biomedical images and text trained on PubMed figure–caption pairs.
  • PORPOISE — Pan-cancer integrative histology-genomic analysis framework using multimodal deep learning for patient stratification.
  • PathomicFusion — Integrated framework fusing histopathology and genomic features via CNN, GNN, and attention gating for cancer diagnosis and prognosis.
  • Virchow — Million-slide digital pathology foundation model using a vision transformer and self-supervised distillation for tile-level pathology image representation.
  • TOAD — Tumor Origin Assessment via Deep-learning; weakly-supervised multi-task model predicting cancer primary origin from H&E whole-slide images.
  • PLIP — Vision-language foundation model for pathology trained with contrastive learning on pathology image–text pairs for image classification and text-to-image retrieval.
  • MUSK — Vision-language foundation model for precision oncology analyzing multimodal paired text and pathology image data for biomarker prediction and retrieval.

Genomics Foundation Models

  • Nucleotide Transformer — Foundation model for genomic sequences across multiple species.
  • DNABERT — Pre-trained bidirectional encoder for DNA sequence analysis.
  • DNABERT-2 — Improved genome foundation model with efficient tokenization.
  • Enformer — Transformer model predicting gene expression from DNA sequence.
  • Basenji — Sequential regulatory activity prediction from DNA sequences.
  • Caduceus — Bidirectional equivariant long-range DNA sequence model based on Mamba.
  • Evo — Long-context genomic foundation model (up to 1M tokens).
  • HyenaDNA — Long-range genomic foundation model handling sequences up to 1M tokens with sub-quadratic attention.
  • Borzoi — Extended successor to Enformer for predicting RNA-seq coverage from long genomic sequence windows (524 kb) with improved resolution.
  • DeepSEA — Deep learning framework for predicting chromatin effects of sequence alterations with single-nucleotide sensitivity across thousands of chromatin features.
  • Sei — Sequence-to-function framework learning a genome-wide regulatory activity code from DNA sequences for variant effect prediction.
  • GPN (Genomic Pre-trained Network) — Masked language model for DNA sequences enabling zero-shot variant effect prediction without requiring functional annotations.

Citation

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Data Schema & Contribution Workflow

  • Data schema reference: docs/data/SCHEMA.md.
  • Source-of-truth workflow:
  • Edit/add resources in README.md.
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    • python scripts/sync_resources_from_readme.py
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