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https://github.com/OmicsML/awesome-foundation-model-single-cell-papers

List: awesome-foundation-model-single-cell-papers
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Created: 2023-06-19T05:24:53.000Z (about 1 year ago)
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awesome-deep-learning-single-cell-papers - [foundation-model-single-cell-papers
README

        ## Foundation-Model-Evaluation-For-Single-cell

1. [2024 Nature Methods] **Assessing GPT-4 for cell type annotation in single-cell RNA-seq analysis** [[paper]](https://www.nature.com/articles/s41592-024-02235-4)

1. [2024 biorxiv] **Metric Mirages in Cell Embeddings** [[paper]](https://www.biorxiv.org/content/10.1101/2024.04.02.587824v1)

1. [2023 biorxiv] **A Deep Dive into Single-Cell RNA Sequencing Foundation Models** [[paper]](https://www.biorxiv.org/content/10.1101/2023.10.19.563100v1.abstract)

1. [2023 bioRxiv scEval] **Evaluating the Utilities of Large Language Models in Single-cell Data Analysis** [[paper]](https://www.biorxiv.org/content/10.1101/2023.09.08.555192v2)

1. [2023 bioRxiv] **Assessing the limits of zero-shot foundation models in single-cell biology** [[paper]](https://www.biorxiv.org/content/10.1101/2023.10.16.561085v1.full.pdf)

1. [2023 bioRxiv] **Foundation Models Meet Imbalanced Single-Cell Data When Learning Cell Type Annotations** [[paper]](https://www.biorxiv.org/content/10.1101/2023.10.24.563625v1)

1. [2023 bioRxiv] **Evaluation of large language models for discovery of gene set function** [[paper]](https://arxiv.org/abs/2309.04019)

1. [2024 ICLR benchmark DNA FD] **BEND: Benchmarking DNA Language Models on Biologically Meaningful Tasks** [[paper]](https://openreview.net/pdf?id=uKB4cFNQFg)

# Foundation-Model-For-Single-cell

1. [2024 biorxiv] **scMulan: a multitask generative pre-trained language model for single-cell analysis** [[paper]](https://www.biorxiv.org/content/10.1101/2024.01.25.577152v1)

1. [2024 biorxiv] **CELLama: Foundation Model for Single Cell and Spatial Transcriptomics by Cell Embedding Leveraging Language Model Abilities** [[paper]](https://www.biorxiv.org/content/10.1101/2024.05.08.593094v1#:~:text=To%20address%20these%20challenges%2C%20we,data%20embedding%20for%20various%20analysis.)

1. [2024 biorxiv] **LangCell: Language-Cell Pre-training for Cell Identity Understanding** [[paper]](https://arxiv.org/pdf/2405.06708)

1. [2024 biorxiv] **Nicheformer: a foundation model for single-cell and spatial omics** [[paper]](https://www.biorxiv.org/content/10.1101/2024.04.15.589472v1)

1. [2024 biorxiv] **Large-scale characterization of cell niches in spatial atlases using bio-inspired graph learning** [[paper]](https://www.biorxiv.org/content/10.1101/2024.02.21.581428v1)

1. [2024 biorxiv] **scmFormer Integrates Large-Scale Single-Cell Proteomics and Transcriptomics Data by Multi-Task Transformer** [[paper]](https://pubmed.ncbi.nlm.nih.gov/38483032/)

1. [2024 biorxiv] **Sequence modeling and design from molecular to genome scale with Evo** [[paper]](https://www.biorxiv.org/content/10.1101/2024.02.27.582234v1)

1. [2024] **Single-cell metadata as language** [[paper]](https://www.nxn.se/valent/2024/2/4/single-cell-metadata-as-language)

1. [2023 NeurIPS] **MuSe-GNN: Learning Unified Gene Representation From Multimodal Biological Graph Data** [[paper]](https://openreview.net/forum?id=4UCktT9XZx)

1. [2023 biorxiv] **scNODE: Generative Model for Temporal Single Cell Transcriptomic Data Prediction** [[paper]](https://www.biorxiv.org/content/10.1101/2023.11.22.568346v1.full.pdf)

1. [2023 biorxiv] **Universal Cell Embeddings: A Foundation Model for Cell Biology** [[paper]](https://www.biorxiv.org/content/10.1101/2023.11.28.568918v1.full.pdf)

1. [2023 NeurIPS 2023 AI for Science Workshop] **scCLIP: Multi-modal Single-cell Contrastive Learning Integration Pre-training** [[paper]](https://openreview.net/pdf?id=KMtM5ZHxct)

1. [2023 NeurIPS 2023 AI for Science Workshop] **Single-cell Masked Autoencoder: An Accurate and Interpretable Automated Immunophenotyper** [[paper]](https://openreview.net/pdf?id=2mq6uezuGj)

1. [2023 biorxiv] **scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis** [[paper]](https://www.biorxiv.org/content/10.1101/2023.12.07.569910v1.full.pdf)

1. [2023 biorxiv] **Large-Scale Cell Representation Learning via Divide-and-Conquer Contrastive Learning** [[paper]](https://arxiv.org/pdf/2306.04371.pdf)

1. [2023 arxiv multimodal] **MuSe-GNN: Learning Unified Gene Representation From Multimodal Biological Graph Data** [[paper]](https://arxiv.org/abs/2310.02275)

1. [2023 Nature Machine Intelligence] **Reusability report: Learning the transcriptional grammar in single-cell RNA-sequencing data using transformers** [[paper]](https://www.nature.com/articles/s42256-023-00757-8)

1. [2023 bioRxiv] **Scalable querying of human cell atlases via a foundational model reveals commonalities across fibrosis-associated macrophages** [[paper]](https://www.biorxiv.org/content/10.1101/2023.07.18.549537v1)

1. [2023 bioRxiv] **To Transformers and Beyond: Large Language Models for the Genome** [[paper]](https://arxiv.org/abs/2311.07621)

1. [2023 bioRxiv] **A pre-trained large generative model for translating single-cell transcriptome to proteome** [[paper]](https://www.biorxiv.org/content/10.1101/2023.07.04.547619v2.full.pdf)

1. [2023 bioRxiv] **GENEPT: A SIMPLE BUT HARD-TO-BEAT FOUNDATION MODEL FOR GENES AND CELLS BUILT FROM CHATGPT** [[paper]](https://www.biorxiv.org/content/10.1101/2023.10.16.562533v1.full.pdf)

1. [2023 bioRxiv] **CellPLM: Pre-training of Cell Language Model Beyond Single Cells** [[paper]](https://www.biorxiv.org/content/10.1101/2023.10.03.560734v1)

1. [2023 Nature Biotechnology multi-modal] **Integration of multi-modal single-cell data** [[Paper]](https://www.nature.com/articles/s41587-023-01826-4)

1. [2023 bioRxiv multi-modal] **Single-cell gene expression prediction from DNA sequence at large contexts** [[paper]](https://www.biorxiv.org/content/10.1101/2023.07.26.550634v1.full)

1. [2023 bioRxiv multi-modal] **Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation** [[paper]](https://www.biorxiv.org/content/10.1101/2023.08.30.555582v1)

1. [2023 bioRxiv] **CellPolaris: Decoding Cell Fate through Generalization Transfer Learning of Gene Regulatory Networks** [[paper]](https://www.biorxiv.org/content/10.1101/2023.09.25.559244v1#:~:text=Applications%20of%20CellPolaris%20demonstrate%20remarkable,outcomes%20in%20cell%20reprogramming%20and)

1. [2023 bioRxiv] **GeneCompass: Deciphering Universal Gene Regulatory Mechanisms with Knowledge-Informed Cross-Species Foundation Model** [[paper]](https://www.biorxiv.org/content/10.1101/2023.09.26.559542v1)

1. [2023 bioRxiv] **scHyena: Foundation Model for Full-Length Single-Cell RNA-Seq Analysis in Brain** [[paper]](https://arxiv.org/abs/2310.02713)

1. [2023 XXXX] **A Deeper Dive into Single-Cell RNA Sequencing Foundation Models** 

1. [2023 bioRxiv] **GET: a foundation model of transcription across human cell types** [[paper]](https://www.biorxiv.org/content/10.1101/2023.09.24.559168v1)

1. [2023 bioRxiv] **Cell2Sentence: Teaching Large Language Models the Language of Biology** [[paper]](https://www.biorxiv.org/content/10.1101/2023.09.11.557287v1)

1. [2023 bioRxiv][**scTranslator**] **A pre-trained large language model for translating single-cell transcriptome to proteome** [[paper]](https://www.biorxiv.org/content/10.1101/2023.07.04.547619v1)

1. [2023 bioRxiv][**scPoli**] **Population-level integration of single-cell datasets enables multi-scale analysis across samples** [[paper]](https://www.biorxiv.org/content/10.1101/2022.11.28.517803v1)

1. [2023 bioRxiv] **Towards Universal Cell Embeddings: Integrating Single-cell RNA-seq Datasets across Species with SATURN** [[paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9915700/)

1. [2023 bioRxiv][**scFoundation**] **Large Scale Foundation Model on Single-cell Transcriptomics** [[paper]](https://www.biorxiv.org/content/10.1101/2023.05.29.542705v2)

1. [2023 Nature][**GeneFormer**] **Transfer learning enables predictions in network biology** [[paper]](https://www.nature.com/articles/s41586-023-06139-9)

1. [2023 iSchience][**tGPT**] **Generative pretraining from large-scale transcriptomes for single-cell deciphering** [[paper]](https://www.sciencedirect.com/science/article/pii/S2589004223006132)

1. [2023 bioRxiv][**scGPT**] **scGPT: Towards Building a Foundation Model for Single-Cell Multi-omics Using Generative AI** [[paper v1]](https://www.biorxiv.org/content/10.1101/2023.04.30.538439v1), [[paper v2]](https://www.biorxiv.org/content/10.1101/2023.04.30.538439v2)

1. [2023 bioRxiv][**xTrimoGene**] **xTrimoGene: An Efficient and Scalable Representation Learner for Single-Cell RNA-Seq Data** [[paper]](https://www.biorxiv.org/content/10.1101/2023.03.24.534055v1)

1. [2022 arxiv][**Exceiver**] **A single-cell gene expression language model** [[paper]](https://arxiv.org/abs/2210.14330)

1. [2022 Nature Machine Intelligence][**scBERT**] **scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data** [[paper]](https://www.nature.com/articles/s42256-022-00534-z)

1. [2022 bioRxiv][**scFormer**] **scFormer: a universal representation learning approach for single-cell data using transformers** [[paper]](https://openreview.net/pdf?id=7hdmA0qtr5)

1. [2022 Bioinformatics][**scPretrain**] **scPretrain: multi-task self-supervised learning for cell-type classification** [[paper]](https://academic.oup.com/bioinformatics/article/38/6/1607/6499287)

# Foundation-Model-For-Pathology

1. [2024 bioRxiv] **BiomedParse: a biomedical foundation model for image parsing of everything everywhere all at once** [[paper]](https://arxiv.org/pdf/2405.12971)

1. [2024 Nature] **A whole-slide foundation model for digital pathology from real-world data** [[paper]](https://www.nature.com/articles/s41586-024-07441-w)

1. [2024 Nature Medicine FM4Pathology] **Towards a general-purpose foundation model for computational pathology** [[paper]](https://www.nature.com/articles/s41591-024-02857-3)

1. [2024 Nature Medicine FM4Pathology] **A visual-language foundation model for computational pathology** [[paper]](https://www.nature.com/articles/s41591-024-02856-4)

1. [2023 Nature Medicine] **A visual–language foundation model for pathology image analysis using medical Twitter** [[paper]](https://www.nature.com/articles/s41591-023-02504-3)