A collection of awesome bio-foundation models, including protein, RNA, DNA, gene, single-cell, and so on.
322
125 commits
updated May 28, 2025
The repository is a collection of awesome bio-foundation modeling papers, various domains include DNA, RNA, gene, protein, single-cell, and multimodalities.
🌟 If you'd like to add a paper or resource, feel free to submit a pull request or open an issue.
The following logo represents:
Papers are ranked chronologically.
DNA & Gene(Enformer) Effective gene expression prediction from sequence by integrating long-range interactions
MoDNA: motif-oriented pre-training for DNA language model
Obtaining genetics insights from deep learning via explainable artificial intelligence
Deciphering microbial gene function using natural language processing
MoDNA: Motif-Oriented Pre-training For DNA Language Model
To Transformers and Beyond: Large Language Models for the Genome
HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution
DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome
Species-aware DNA language modeling
DNA language models are powerful predictors of genome-wide variant effects
GPN-MSA: an alignment-based DNA language model for genome-wide variant effect prediction
GENA-LM: A Family of Open-Source Foundational DNA Language Models for Long Sequences
EpiGePT: a Pretrained Transformer model for epigenomics
DNAGPT: A Generalized Pre-trained Tool for Multiple DNA Sequence Analysis Tasks
The Nucleotide Transformer: Building and Evaluating Robust Foundation Models for Human Genomics
DNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genome
DNABERT-S: Learning Species-Aware DNA Embedding with Genome Foundation Models
Single-cell gene expression prediction from DNA sequence at large contexts
Genomic language model predicts protein co-regulation and function
RNAClustering and classification methods for single-cell RNA-sequencing data
EMDLP: Ensemble multiscale deep learning model for RNA methylation site prediction
scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data
(GeneFormer) Transfer learning enables predictions in network biology
GeneCompass: Deciphering Universal Gene Regulatory Mechanisms with Knowledge-Informed Cross-Species Foundation Model
scPRINT: pre-training on 50 million cells allows robust gene network predictions
scPML: pathway-based multi-view learning for cell type annotation from single-cell RNA-seq data
(RNA-FM) Interpretable RNA Foundation Model from Unannotated Data for Highly Accurate RNA Structure and Function Predictions
(RNABERT) Informative RNA base embedding for RNA structural alignment and clustering by deep representation learning
(MRM-BERT) Prediction of Multiple Types of RNA Modifications via Biological Language Model
(SpliceBERT) Self-supervised learning on millions of pre-mRNA sequences improves sequence-based RNA splicing prediction
UNI-RNA: universal pre-trained models revolutionize RNA research
A Deep Dive into Single-Cell RNA Sequencing Foundation Models
xTrimoGene: An Efficient and Scalable Representation Learner for Single-Cell RNA-Seq Data
trRosettaRNA: automated prediction of RNA 3D structure with transformer network
(RfamGen) Deep generative design of RNA family sequences
(RNA-MFM) Multiple sequence alignment-based RNA language model and its application to structural inference
RiNALMo: General-Purpose RNA Language Models Can Generalize Well on Structure Prediction Tasks
ATOM-1: A Foundation Model for RNA Structure and Function Built on Chemical Mapping Data
RNAformer: A Simple Yet Effective Deep LearningModel for RNA Secondary Structure Prediction
scDisInFact: disentangled learning for integration and prediction of multi-batch multi-condition single-cell RNA-sequencing data
CellPLM: Pre-training of Cell Language Model Beyond Single Cells
(mLLMCelltype) Large Language Model Consensus Substantially Improves the Cell Type Annotation Accuracy for scRNA-seq Data
Assessing GPT-4 for cell type annotation in single-cell RNA-seq analysis
A 5′ UTR language model for decoding untranslated regions of mRNA and function predictions
ERNIE-RNA: An RNA Language Model with Structure-enhanced Representations
GenerRNA: A generative pre-trained language model for de novo RNA design
Orthrus: Towards Evolutionary and Functional RNA Foundation Models
ProteinParapred: antibody paratope prediction using convolutional and recurrent neural networks
(UniRep) Unified rational protein engineering with sequence-based deep representation learning
(TAPE) Evaluating Protein Transfer Learning with TAPE
ProtTrans: Towards Cracking the Language of Life’s Code Through Self-Supervised Deep Learning and High Performance Computing
(ESM) Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
(ESM-1v) Language models enable zero-shot prediction of the effects of mutations on protein function
(IgLM) Generative language modeling for antibody design
(ESM-2 & ESMFold) Language models of protein sequences at the scale of evolution enable accurate structure prediction
ProtGPT2 is a deep unsupervised language model for protein design
ProteinBERT: a universal deep-learning model of protein sequence and function
OntoProtein: Protein Pretraining With Gene Ontology Embedding
(AntiBERTa) Deciphering the language of antibodies using self-supervised learning
AbLang: an antibody language model for completing antibody sequences
ProGen2: Exploring the boundaries of protein language models
SaProt: Protein Language Modeling with Structure-aware Vocabulary
Ankh: Optimized Protein Language Model Unlocks General-Purpose Modelling
(GearNet) Protein Representation Learning by Geometric Structure Pretraining
ProtST: Multi-Modality Learning of Protein Sequences and Biomedical Texts
Efficient evolution of human antibodies from general protein language models
(CARP) Convolutions are competitive with transformers for protein sequence pretraining
(HelixFold-Single) A method for multiple-sequence-alignment-free protein structure prediction using a protein language model
(ABGNN) Pre-training Antibody Language Models for Antigen-Specific Computational Antibody Design
(ReprogBert) Reprogramming Pretrained Language Models for Antibody Sequence Infilling
ProteinFlow: a Python Library to Pre-Process Protein Structure Data for Deep Learning Applications
xTrimoPGLM: Unified 100B-Scale Pre-trained Transformer for Deciphering the Language of Protein
ProLLaMA: A Protein Large Language Model for Multi-Task Protein Language Processing
(ESM-GearNet) A Systematic Study of Joint Representation Learning on Protein Sequences and Structures
(ProteinINR) Pre-training Sequence, Structure, and Surface Features for Comprehensive Protein Representation Learning
(CaLM) Codon language embeddings provide strong signals for use in protein engineering
(DeepGo) Protein function prediction as approximate semantic entailment
PLMSearch: Protein language model powers accurate and fast sequence search for remote homology
Genomic language model predicts protein co-regulation and function
Prot2Token: A multi-task framework for protein language processing using autoregressive language modeling
(ESM-3) Simulating 500 million years of evolution with a language model
Training Compute-Optimal Protein Language Models
Fine-tuning protein language models boosts predictions across diverse tasks
Contextual AI models for single-cell protein biology
Sequence-to-sequence translation from mass spectra to peptides with a transformer model
OpenFold: retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization
ProteinInvBench: Benchmarking Protein Inverse Folding on Diverse Tasks, Models, and Metrics
ProteinShake: Building datasets and benchmarks for deep learning on protein structures
ProteinBench: A Holistic Evaluation of Protein Foundation Models
An end-to-end framework for the prediction of protein structure and fitness from single sequence
SFM-Protein: Integrative Co-evolutionary Pre-training for Advanced Protein Sequence Representation
(BioEmu-1) Scalable emulation of protein equilibrium ensembles with generative deep learning
(Evolla) Decoding the Molecular Language of Proteins with Evolla
ProTrek: Navigating the protein universe through tri-modal contrastive learning
(ProteinDT) A text-guided protein design framework
(Pinal) Toward De Novo Protein Design from Natural Language
Protein foundation models are hot topics, more papers can be found in
Single-cell(DCell) Using deep learning to model the hierarchical structure and function of a cell
scVAE: variational auto-encoders for single-cell gene expression data
A sandbox for prediction and integration of DNA, RNA, and proteins in single cells
scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data
scPML: pathway-based multi-view learning for cell type annotation from single-cell RNA-seq data
(scFoundation) Large Scale Foundation Model on Single-cell Transcriptomics
(DPI) Modeling and analyzing single-cell multimodal data with deep parametric inference
(ScPROTEIN) A Versatile Deep Graph Contrastive Learning Framework for Single-cell Proteomics Embedding
scGPT: toward building a foundation model for single-cell multi-omics using generative AI
scMulan: a multitask generative pre-trained language model for single-cell analysis
scDiffusion: conditional generation of high-quality single-cell data using diffusion model
Cell2Sentence: Teaching Large Language Models the Language of Biology
CellPLM: Pre-training of Cell Language Model Beyond Single Cells
Assessing GPT-4 for cell type annotation in single-cell RNA-seq analysis
Multimodalities(DPI) Modeling and analyzing single-cell multimodal data with deep parametric inference
Pretraining model for biological sequence data
BioSeq-BLM: a platform for analyzing DNA, RNA and protein sequences based on biological language models
A sandbox for prediction and integration of DNA, RNA, and proteins in single cells
Galactica: A Large Language Model for Science
BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associations
DARWIN Series: Domain Specific Large Language Models for Natural Science
(scMoFormer) Single-Cell Multimodal Prediction via Transformers
BioT5+: Towards Generalized Biological Understanding with IUPAC Integration and Multi-task Tuning
ChatCell: Facilitating Single-Cell Analysis with Natural Language
(Evo) Sequence modeling and design from molecular to genome scale with Evo
NatureLM: Deciphering the Language of Nature for Scientific Discovery
Genome modeling and design across all domains of life with Evo 2
PathologyH-optimus-0: Extract powerful features from histology images for various downstream applications
Related SurveysLearning the protein language: Evolution, structure, and function
Protein Language Models and Structure Prediction: Connection and Progression
Progress and Opportunities of Foundation Models in Bioinformatics
Deep Learning for Genomics: From Early Neural Nets to Modern Large Language Models
Applications of transformer-based language models in bioinformatics: a survey
Best practices for single-cell analysis across modalities
Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey
Scientific Large Language Models: A Survey on Biological & Chemical Domains
Large language models in bioinformatics: applications and perspectives
Leveraging Biomolecule and Natural Language through Multi-Modal Learning: A Survey
Machine learning for functional protein design
Related Repositories229 followers · starred Mar 2024
105 followers · starred Mar 2024
7 followers · starred Jan 2025
A collection of awesome bio-foundation models, including protein, RNA, DNA, gene, single-cell, and so on.
322
125 commits
updated May 28, 2025
The repository is a collection of awesome bio-foundation modeling papers, various domains include DNA, RNA, gene, protein, single-cell, and multimodalities.
🌟 If you'd like to add a paper or resource, feel free to submit a pull request or open an issue.
The following logo represents:
Papers are ranked chronologically.
DNA & Gene(Enformer) Effective gene expression prediction from sequence by integrating long-range interactions
MoDNA: motif-oriented pre-training for DNA language model
Obtaining genetics insights from deep learning via explainable artificial intelligence
Deciphering microbial gene function using natural language processing
MoDNA: Motif-Oriented Pre-training For DNA Language Model
To Transformers and Beyond: Large Language Models for the Genome
HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution
DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome
Species-aware DNA language modeling
DNA language models are powerful predictors of genome-wide variant effects
GPN-MSA: an alignment-based DNA language model for genome-wide variant effect prediction
GENA-LM: A Family of Open-Source Foundational DNA Language Models for Long Sequences
EpiGePT: a Pretrained Transformer model for epigenomics
DNAGPT: A Generalized Pre-trained Tool for Multiple DNA Sequence Analysis Tasks
The Nucleotide Transformer: Building and Evaluating Robust Foundation Models for Human Genomics
DNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genome
DNABERT-S: Learning Species-Aware DNA Embedding with Genome Foundation Models
Single-cell gene expression prediction from DNA sequence at large contexts
Genomic language model predicts protein co-regulation and function
RNAClustering and classification methods for single-cell RNA-sequencing data
EMDLP: Ensemble multiscale deep learning model for RNA methylation site prediction
scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data
(GeneFormer) Transfer learning enables predictions in network biology
GeneCompass: Deciphering Universal Gene Regulatory Mechanisms with Knowledge-Informed Cross-Species Foundation Model
scPRINT: pre-training on 50 million cells allows robust gene network predictions
scPML: pathway-based multi-view learning for cell type annotation from single-cell RNA-seq data
(RNA-FM) Interpretable RNA Foundation Model from Unannotated Data for Highly Accurate RNA Structure and Function Predictions
(RNABERT) Informative RNA base embedding for RNA structural alignment and clustering by deep representation learning
(MRM-BERT) Prediction of Multiple Types of RNA Modifications via Biological Language Model
(SpliceBERT) Self-supervised learning on millions of pre-mRNA sequences improves sequence-based RNA splicing prediction
UNI-RNA: universal pre-trained models revolutionize RNA research
A Deep Dive into Single-Cell RNA Sequencing Foundation Models
xTrimoGene: An Efficient and Scalable Representation Learner for Single-Cell RNA-Seq Data
trRosettaRNA: automated prediction of RNA 3D structure with transformer network
(RfamGen) Deep generative design of RNA family sequences
(RNA-MFM) Multiple sequence alignment-based RNA language model and its application to structural inference
RiNALMo: General-Purpose RNA Language Models Can Generalize Well on Structure Prediction Tasks
ATOM-1: A Foundation Model for RNA Structure and Function Built on Chemical Mapping Data
RNAformer: A Simple Yet Effective Deep LearningModel for RNA Secondary Structure Prediction
scDisInFact: disentangled learning for integration and prediction of multi-batch multi-condition single-cell RNA-sequencing data
CellPLM: Pre-training of Cell Language Model Beyond Single Cells
(mLLMCelltype) Large Language Model Consensus Substantially Improves the Cell Type Annotation Accuracy for scRNA-seq Data
Assessing GPT-4 for cell type annotation in single-cell RNA-seq analysis
A 5′ UTR language model for decoding untranslated regions of mRNA and function predictions
ERNIE-RNA: An RNA Language Model with Structure-enhanced Representations
GenerRNA: A generative pre-trained language model for de novo RNA design
Orthrus: Towards Evolutionary and Functional RNA Foundation Models
ProteinParapred: antibody paratope prediction using convolutional and recurrent neural networks
(UniRep) Unified rational protein engineering with sequence-based deep representation learning
(TAPE) Evaluating Protein Transfer Learning with TAPE
ProtTrans: Towards Cracking the Language of Life’s Code Through Self-Supervised Deep Learning and High Performance Computing
(ESM) Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
(ESM-1v) Language models enable zero-shot prediction of the effects of mutations on protein function
(IgLM) Generative language modeling for antibody design
(ESM-2 & ESMFold) Language models of protein sequences at the scale of evolution enable accurate structure prediction
ProtGPT2 is a deep unsupervised language model for protein design
ProteinBERT: a universal deep-learning model of protein sequence and function
OntoProtein: Protein Pretraining With Gene Ontology Embedding
(AntiBERTa) Deciphering the language of antibodies using self-supervised learning
AbLang: an antibody language model for completing antibody sequences
ProGen2: Exploring the boundaries of protein language models
SaProt: Protein Language Modeling with Structure-aware Vocabulary
Ankh: Optimized Protein Language Model Unlocks General-Purpose Modelling
(GearNet) Protein Representation Learning by Geometric Structure Pretraining
ProtST: Multi-Modality Learning of Protein Sequences and Biomedical Texts
Efficient evolution of human antibodies from general protein language models
(CARP) Convolutions are competitive with transformers for protein sequence pretraining
(HelixFold-Single) A method for multiple-sequence-alignment-free protein structure prediction using a protein language model
(ABGNN) Pre-training Antibody Language Models for Antigen-Specific Computational Antibody Design
(ReprogBert) Reprogramming Pretrained Language Models for Antibody Sequence Infilling
ProteinFlow: a Python Library to Pre-Process Protein Structure Data for Deep Learning Applications
xTrimoPGLM: Unified 100B-Scale Pre-trained Transformer for Deciphering the Language of Protein
ProLLaMA: A Protein Large Language Model for Multi-Task Protein Language Processing
(ESM-GearNet) A Systematic Study of Joint Representation Learning on Protein Sequences and Structures
(ProteinINR) Pre-training Sequence, Structure, and Surface Features for Comprehensive Protein Representation Learning
(CaLM) Codon language embeddings provide strong signals for use in protein engineering
(DeepGo) Protein function prediction as approximate semantic entailment
PLMSearch: Protein language model powers accurate and fast sequence search for remote homology
Genomic language model predicts protein co-regulation and function
Prot2Token: A multi-task framework for protein language processing using autoregressive language modeling
(ESM-3) Simulating 500 million years of evolution with a language model
Training Compute-Optimal Protein Language Models
Fine-tuning protein language models boosts predictions across diverse tasks
Contextual AI models for single-cell protein biology
Sequence-to-sequence translation from mass spectra to peptides with a transformer model
OpenFold: retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization
ProteinInvBench: Benchmarking Protein Inverse Folding on Diverse Tasks, Models, and Metrics
ProteinShake: Building datasets and benchmarks for deep learning on protein structures
ProteinBench: A Holistic Evaluation of Protein Foundation Models
An end-to-end framework for the prediction of protein structure and fitness from single sequence
SFM-Protein: Integrative Co-evolutionary Pre-training for Advanced Protein Sequence Representation
(BioEmu-1) Scalable emulation of protein equilibrium ensembles with generative deep learning
(Evolla) Decoding the Molecular Language of Proteins with Evolla
ProTrek: Navigating the protein universe through tri-modal contrastive learning
(ProteinDT) A text-guided protein design framework
(Pinal) Toward De Novo Protein Design from Natural Language
Protein foundation models are hot topics, more papers can be found in
Single-cell(DCell) Using deep learning to model the hierarchical structure and function of a cell
scVAE: variational auto-encoders for single-cell gene expression data
A sandbox for prediction and integration of DNA, RNA, and proteins in single cells
scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data
scPML: pathway-based multi-view learning for cell type annotation from single-cell RNA-seq data
(scFoundation) Large Scale Foundation Model on Single-cell Transcriptomics
(DPI) Modeling and analyzing single-cell multimodal data with deep parametric inference
(ScPROTEIN) A Versatile Deep Graph Contrastive Learning Framework for Single-cell Proteomics Embedding
scGPT: toward building a foundation model for single-cell multi-omics using generative AI
scMulan: a multitask generative pre-trained language model for single-cell analysis
scDiffusion: conditional generation of high-quality single-cell data using diffusion model
Cell2Sentence: Teaching Large Language Models the Language of Biology
CellPLM: Pre-training of Cell Language Model Beyond Single Cells
Assessing GPT-4 for cell type annotation in single-cell RNA-seq analysis
Multimodalities(DPI) Modeling and analyzing single-cell multimodal data with deep parametric inference
Pretraining model for biological sequence data
BioSeq-BLM: a platform for analyzing DNA, RNA and protein sequences based on biological language models
A sandbox for prediction and integration of DNA, RNA, and proteins in single cells
Galactica: A Large Language Model for Science
BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associations
DARWIN Series: Domain Specific Large Language Models for Natural Science
(scMoFormer) Single-Cell Multimodal Prediction via Transformers
BioT5+: Towards Generalized Biological Understanding with IUPAC Integration and Multi-task Tuning
ChatCell: Facilitating Single-Cell Analysis with Natural Language
(Evo) Sequence modeling and design from molecular to genome scale with Evo
NatureLM: Deciphering the Language of Nature for Scientific Discovery
Genome modeling and design across all domains of life with Evo 2
PathologyH-optimus-0: Extract powerful features from histology images for various downstream applications
Related SurveysLearning the protein language: Evolution, structure, and function
Protein Language Models and Structure Prediction: Connection and Progression
Progress and Opportunities of Foundation Models in Bioinformatics
Deep Learning for Genomics: From Early Neural Nets to Modern Large Language Models
Applications of transformer-based language models in bioinformatics: a survey
Best practices for single-cell analysis across modalities
Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey
Scientific Large Language Models: A Survey on Biological & Chemical Domains
Large language models in bioinformatics: applications and perspectives
Leveraging Biomolecule and Natural Language through Multi-Modal Learning: A Survey
Machine learning for functional protein design
Related Repositories229 followers · starred Mar 2024
105 followers · starred Mar 2024
7 followers · starred Jan 2025