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.
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(Enformer) Effective gene expression prediction from sequence by integrating long-range interactions
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MoDNA: motif-oriented pre-training for DNA language model
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Obtaining genetics insights from deep learning via explainable artificial intelligence
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Deciphering microbial gene function using natural language processing
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MoDNA: Motif-Oriented Pre-training For DNA Language Model
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To Transformers and Beyond: Large Language Models for the Genome
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GeneCompass: Deciphering Universal Gene Regulatory Mechanisms with Knowledge-Informed Cross-Species Foundation Model
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HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution
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(GeneFormer) Transfer learning enables predictions in network biology
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DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome
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Species-aware DNA language modeling
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DNA language models are powerful predictors of genome-wide variant effects
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GPN-MSA: an alignment-based DNA language model for genome-wide variant effect prediction
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GENA-LM: A Family of Open-Source Foundational DNA Language Models for Long Sequences
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EpiGePT: a Pretrained Transformer model for epigenomics
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DNAGPT: A Generalized Pre-trained Tool for Multiple DNA Sequence Analysis Tasks
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The Nucleotide Transformer: Building and Evaluating Robust Foundation Models for Human Genomics
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DNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genome
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DNABERT-S: Learning Species-Aware DNA Embedding with Genome Foundation Models
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Single-cell gene expression prediction from DNA sequence at large contexts
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Genomic language model predicts protein co-regulation and function
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Clustering and classification methods for single-cell RNA-sequencing data
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EMDLP: Ensemble multiscale deep learning model for RNA methylation site prediction
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scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data
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scPML: pathway-based multi-view learning for cell type annotation from single-cell RNA-seq data
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(RNA-FM) Interpretable RNA Foundation Model from Unannotated Data for Highly Accurate RNA Structure and Function Predictions
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(RNABERT) Informative RNA base embedding for RNA structural alignment and clustering by deep representation learning
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(MRM-BERT) Prediction of Multiple Types of RNA Modifications via Biological Language Model
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(SpliceBERT) Self-supervised learning on millions of pre-mRNA sequences improves sequence-based RNA splicing prediction
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UNI-RNA: universal pre-trained models revolutionize RNA research
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A Deep Dive into Single-Cell RNA Sequencing Foundation Models
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xTrimoGene: An Efficient and Scalable Representation Learner for Single-Cell RNA-Seq Data
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trRosettaRNA: automated prediction of RNA 3D structure with transformer network
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(RfamGen) Deep generative design of RNA family sequences
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(RNA-MFM) Multiple sequence alignment-based RNA language model and its application to structural inference
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RiNALMo: General-Purpose RNA Language Models Can Generalize Well on Structure Prediction Tasks
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ATOM-1: A Foundation Model for RNA Structure and Function Built on Chemical Mapping Data
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RNAformer: A Simple Yet Effective Deep LearningModel for RNA Secondary Structure Prediction
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scDisInFact: disentangled learning for integration and prediction of multi-batch multi-condition single-cell RNA-sequencing data
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CellPLM: Pre-training of Cell Language Model Beyond Single Cells
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Assessing GPT-4 for cell type annotation in single-cell RNA-seq analysis
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A 5′ UTR language model for decoding untranslated regions of mRNA and function predictions
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ERNIE-RNA: An RNA Language Model with Structure-enhanced Representations
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Parapred: antibody paratope prediction using convolutional and recurrent neural networks
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(UniRep) Unified rational protein engineering with sequence-based deep representation learning
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(TAPE) Evaluating Protein Transfer Learning with TAPE
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ProtTrans: Towards Cracking the Language of Life’s Code Through Self-Supervised Deep Learning and High Performance Computing
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(ESM) Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
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(ESM-1v) Language models enable zero-shot prediction of the effects of mutations on protein function
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(IgLM) Generative language modeling for antibody design
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(ESM-2 & ESMFold) Language models of protein sequences at the scale of evolution enable accurate structure prediction
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ProtGPT2 is a deep unsupervised language model for protein design
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ProteinBERT: a universal deep-learning model of protein sequence and function
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OntoProtein: Protein Pretraining With Gene Ontology Embedding
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(AntiBERTa) Deciphering the language of antibodies using self-supervised learning
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AbLang: an antibody language model for completing antibody sequences
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ProGen2: Exploring the boundaries of protein language models
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SaProt: Protein Language Modeling with Structure-aware Vocabulary
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Ankh: Optimized Protein Language Model Unlocks General-Purpose Modelling
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(GearNet) Protein Representation Learning by Geometric Structure Pretraining
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ProtST: Multi-Modality Learning of Protein Sequences and Biomedical Texts
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Efficient evolution of human antibodies from general protein language models
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(CARP) Convolutions are competitive with transformers for protein sequence pretraining
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(HelixFold-Single) A method for multiple-sequence-alignment-free protein structure prediction using a protein language model
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(ABGNN) Pre-training Antibody Language Models for Antigen-Specific Computational Antibody Design
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(ReprogBert) Reprogramming Pretrained Language Models for Antibody Sequence Infilling
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xTrimoPGLM: Unified 100B-Scale Pre-trained Transformer for Deciphering the Language of Protein
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ProLLaMA: A Protein Large Language Model for Multi-Task Protein Language Processing
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(ESM-GearNet) A Systematic Study of Joint Representation Learning on Protein Sequences and Structures
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(ProteinINR) Pre-training Sequence, Structure, and Surface Features for Comprehensive Protein Representation Learning
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(CaLM) Codon language embeddings provide strong signals for use in protein engineering
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(DeepGo) Protein function prediction as approximate semantic entailment
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PLMSearch: Protein language model powers accurate and fast sequence search for remote homology
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Genomic language model predicts protein co-regulation and function
Protein foundation models are hot topics, more papers can be found in
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(DCell) Using deep learning to model the hierarchical structure and function of a cell
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scVAE: variational auto-encoders for single-cell gene expression data
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A sandbox for prediction and integration of DNA, RNA, and proteins in single cells
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scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data
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scPML: pathway-based multi-view learning for cell type annotation from single-cell RNA-seq data
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(scFoundation) Large Scale Foundation Model on Single-cell Transcriptomics
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(DPI) Modeling and analyzing single-cell multimodal data with deep parametric inference
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(ScPROTEIN) A Versatile Deep Graph Contrastive Learning Framework for Single-cell Proteomics Embedding
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scGPT: toward building a foundation model for single-cell multi-omics using generative AI
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scMulan: a multitask generative pre-trained language model for single-cell analysis
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scDiffusion: conditional generation of high-quality single-cell data using diffusion model
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Cell2Sentence: Teaching Large Language Models the Language of Biology
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CellPLM: Pre-training of Cell Language Model Beyond Single Cells
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Assessing GPT-4 for cell type annotation in single-cell RNA-seq analysis
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(DPI) Modeling and analyzing single-cell multimodal data with deep parametric inference
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Pretraining model for biological sequence data
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BioSeq-BLM: a platform for analyzing DNA, RNA and protein sequences based on biological language models
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A sandbox for prediction and integration of DNA, RNA, and proteins in single cells
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Galactica: A Large Language Model for Science
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BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associations
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DARWIN Series: Domain Specific Large Language Models for Natural Science
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(scMoFormer) Single-Cell Multimodal Prediction via Transformers
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BioT5+: Towards Generalized Biological Understanding with IUPAC Integration and Multi-task Tuning
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ChatCell: Facilitating Single-Cell Analysis with Natural Language
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(Evo) Sequence modeling and design from molecular to genome scale with Evo
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Learning the protein language: Evolution, structure, and function
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Protein Language Models and Structure Prediction: Connection and Progression
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Progress and Opportunities of Foundation Models in Bioinformatics
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Deep Learning for Genomics: From Early Neural Nets to Modern Large Language Models
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Applications of transformer-based language models in bioinformatics: a survey
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Best practices for single-cell analysis across modalities
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Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey
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Scientific Large Language Models: A Survey on Biological & Chemical Domains
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Large language models in bioinformatics: applications and perspectives
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Leveraging Biomolecule and Natural Language through Multi-Modal Learning: A Survey
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Machine learning for functional protein design
- Machine-Learning-for-Proteins
- Scientific-Large-Language-Models
- Awesome-Scientific-Language-Models
- Awesome-Biomolecule-Language-Cross-Modeling
- Awesome-Deep-Learning-Single-Cell-Papers
- Awesome-Protein-Representation-Learning
- Awesome-Molecule-Protein-Pretrain-Papers
- Awesome-Pretrain-on-Molecules
- Awesome-Molecule-Text
- Aswesome-Deep-Learing-for-Life-Sciences
- Awesome-Docking
- Awesome-Biology
- Awesome-Single-Cell
- Awesome-Computational-Biology
- Awesome-Bioinformatics
- Awesome-Multi-Omics
- LLM4ScientificDiscovery
- LLM4Mol