Foundation-Model-Evaluation-For-Single-cell

1. [2025 Genome Biology] Biology-driven insights into the power of single-cell foundation models [paper]
2. [2025 arXiv] BMFM-RNA: An Open Framework for Building and Evaluating Transcriptomic Foundation Models [paper]
3. [2025 biorxiv] Benchmarking gene embeddings from sequence, expression, network, and text models for functional prediction tasks [paper]
4. [2025 biorxiv] Diversity by Design: Addressing Mode Collapse Improves scRNA-seq Perturbation Modeling on Well-Calibrated Metrics [paper]
5. [2025 Genome Biology] Zero-shot evaluation reveals limitations of single-cell foundation models [paper]
6. [2025 biorxiv] scDrugMap: Benchmarking Large Foundation Models for Drug Response Prediction [paper]
7. [2025 WSDM] A Systematic Evaluation of Single-Cell Foundation Models on Cell-Type Classification Task [paper]
8. [2024 Nature Machine Intelligence] Delineating the effective use of self-supervised learning in single-cell genomics [paper]
9. [2024 Patterns] BioLLM: A standardized framework for integrating and benchmarking single-cell foundation models [paper]
10. [2024 biorxiv] Evaluating the role of pre-training dataset size and diversity on single-cell foundation model performance [paper]
11. [2024 Nature Machine Intelligence] Deeper evaluation of a single-cell foundation models [paper]
12. [2024 Nature Methods] Assessing GPT-4 for cell type annotation in single-cell RNA-seq analysis [paper]
13. [2024 biorxiv] Metric Mirages in Cell Embeddings [paper]
14. [2023 Nature Machine Intelligence] Reusability report: Learning the transcriptional grammar in single-cell RNA-sequencing data using transformers [paper]
15. [2023 biorxiv] A Deep Dive into Single-Cell RNA Sequencing Foundation Models [paper]
16. [2023 bioRxiv scEval] Evaluating the Utilities of Large Language Models in Single-cell Data Analysis [paper]
17. [2023 bioRxiv] Foundation Models Meet Imbalanced Single-Cell Data When Learning Cell Type Annotations [paper]
18. [2023 bioRxiv] Evaluation of large language models for discovery of gene set function [paper]
19. [2024 ICLR benchmark DNA FD] BEND: Benchmarking DNA Language Models on Biologically Meaningful Tasks [paper]

Single-cell-Genomics-Machine-Learning-Related-Survey-and-Perspective

1. [2025 bioRxiv] Large Language Models Meet Virtual Cell: A Survey [paper]
2. [2025 Patterns] Large language models for drug discovery and development [paper]
3. [2025 Experimental & Molecular Medicine] Single-cell foundation models: bringing artificial intelligence into cell biology [paper]
4. [2025 ACL] A survey on foundation language models for single-cell biology [paper]
5. [2025 Nature Machine Intelligence] Transformers and genome language models [paper]
6. [2024 Cell, FM4perturbation data: a review] Toward a foundation model of causal cell and tissue biology with a Perturbation Cell and Tissue Atlas [paper]
7. [2024 National Science Review] General-purpose pre-trained large cellular models for single-cell transcriptomic [paper]
8. [2024 Nature Methods] Transformers in single-cell omics: a review and new perspectives [paper]
9. [2024 Cell] The future of rapid and automated single-cell data analysis using reference mapping [paper]
10. [2024 Nature] The Human Cell Atlas from a cell census to a unified foundation model [paper]
11. [2024 Cell] How to build the virtual cell with artificial intelligence: Priorities and opportunities [paper]
12. [2024 Computational and Structural Biotechnology Journal] A mini-review on perturbation modelling across single-cell omic modalities [paper]

Foundation-Model-For-Single-cell

FM + LLM

1. [2025 BioRxiv] Large Language Model Consensus Substantially Improves the Cell Type Annotation Accuracy for scRNA-seq Data [paper]
2. [2025 biorxiv] Towards Applying Large Language Models to Complement Single-Cell Foundation Models [paper]
3. [2025 biorxiv] Language-Enhanced Representation Learning for Single-Cell Transcriptomics [paper]
4. [2025 ICML] sciLaMA: A Single-Cell Representation Learning Framework to Leverage Prior Knowledge from Large Language Models [paper]
5. [2025 BioRxiv] Scaling Large Language Models for Next-Generation Single-Cell Analysis [paper]
6. [2025 Nature Biomedical Engineering] Simple and effective embedding model for single-cell biology built from chatgpt [paper]
7. [2025 ICML Workshop GenBio] TEDDY: A FAMILY OF FOUNDATION MODELS FOR UNDERSTANDING SINGLE CELL BIOLOGY [paper]
8. [2024 ICLR Workshop MLGenX] Joint embedding of transcriptomes and text enables interactive single-cell RNA-seq data exploration via natural language [paper]
9. [2024 biorxiv] CELLama: Foundation Model for Single Cell and Spatial Transcriptomics by Cell Embedding Leveraging Language Model Abilities [paper]
10. [2024 BioRxiv] scChat: A Large Language Model-Powered Co-Pilot for Contextualized Single-Cell RNA Sequencing Analysis [paper]
11. [2024 ICML] Cell2Sentence: Teaching Large Language Models the Language of Biology [paper]
12. [2024 biorxiv] Multimodal learning of transcriptomes and text enables interactive single-cell RNA-seq data exploration with natural-language chats [paper]
13. [2024 arxiv] scReader: Prompting Large Language Models to Interpret scRNA-seq Data [paper]
14. [2024 BioRxiv] CASSIA: a multi-agent large language model for reference free, interpretable, and automated cell annotation of single-cell RNA-sequencing data [paper]
15. [2024 Nature Methods] Assessing GPT-4 for cell type annotation in single-cell RNA-seq analysis [paper]

FM

1. [2025 biorxiv] scLinguist: A pre-trained hyena-based foundation model for cross-modality translation in single-cell multi-omics [paper]
2. [2025 Nature] A foundation model of transcription across human cell types [paper]
3. [2025 Nature Communications] scPRINT: pre-training on 50 million cells allows robust gene network predictions [paper]
4. [2025 Nature Methods] A visual–omics foundation model to bridge histopathology with spatial transcriptomics [paper]
5. [2025 Nature Methods] EpiAgent: foundation model for single-cell epigenomics [paper]
6. [2025 biorxiv] CAPTAIN: A multimodal foundation model pretrained on co-assayed single-cell RNA and protein [paper]
7. [2025 biorxiv] scGPT-spatial: Continual Pretraining of Single-Cell Foundation Model for Spatial Transcriptomics [paper]
8. [2025 BioRxiv] SToFM: a Multi-scale Foundation Model for Spatial Transcriptomics [paper]
9. [2025 BioRxiv] SCARF: Single Cell ATAC-seq and RNA-seq Foundation model [paper]
10. [2025 BioRxiv] Multimodal foundation model predicts zero-shot functional perturbations and cell fate dynamics [paper]
11. [2025 Nature Communications] CellFM: a large-scale foundation model pre-trained on transcriptomics of 100 million human cells [paper]
12. [2025 NeurIPS] Tabula: A Tabular Self-Supervised Foundation Model for Single-Cell Transcriptomics [paper]
13. [2025 Nature Biomedical Engineering] A pre-trained large generative model for translating single-cell transcriptome to proteome [paper]
14. [2025 BioRxiv, Tabular Modeling on single-cell data] Toward a privacy-preserving predictive foundation model of single-cell transcriptomics with federated learning and tabular modeling [paper]
15. [2025 Nature Methods] scNET: learning context-specific gene and cell embeddings by integrating single-cell gene expression data with protein–protein interactions [paper]
16. [2025 RECOMB] Learning multi-cellular representations of single-cell transcriptomics data enables characterization of patient-level disease states [paper]
17. [2024 Nature Methods][scFoundation] Large Scale Foundation Model on Single-cell Transcriptomics [paper]
18. [2024 Nature Methods] scPROTEIN: a versatile deep graph contrastive learning framework for single-cell proteomics embedding [paper]
19. [2024 biorxiv] scLong: A Billion-Parameter Foundation Model for Capturing Long-Range Gene Context in Single-Cell Transcriptomics [paper]
20. [2024 National Science Review] Cell-GraphCompass: Modeling Single Cells with Graph Structure Foundation Model [paper]
21. [2024 Nature] A cell atlas foundation model for scalable search of similar human cells [paper]
22. [2024 BioRxiv] Scaling Dense Representations for Single Cell with Transcriptome-Scale Context [paper]
23. [2024 BioRxiv] A framework for gene representation on spatial transcriptomics [paper]
24. [2024 BioRxiv] CancerFoundation: A single-cell RNA sequencing foundation model to decipher drug resistance in cancer [paper]
25. [2024 NeurIPS] Cell-ontology guided transcriptome foundation model [paper]
26. [2024 RECOMB] scMulan: a multitask generative pre-trained language model for single-cell analysis [paper]
27. [2024 Nature Methods][scGPT] scGPT: toward building a foundation model for single-cell multi-omics using generative AI [paper]
28. [2024 ICML] LangCell: Language-Cell Pre-training for Cell Identity Understanding [paper]
29. [2024 biorxiv] Nicheformer: a foundation model for single-cell and spatial omics [paper]
30. [2024 biorxiv] Large-scale characterization of cell niches in spatial atlases using bio-inspired graph learning [paper]
31. [2024 biorxiv] scmFormer Integrates Large-Scale Single-Cell Proteomics and Transcriptomics Data by Multi-Task Transformer [paper]
32. [2024] Single-cell metadata as language [paper]
33. [2024 Cell Research] GeneCompass: Deciphering Universal Gene Regulatory Mechanisms with Knowledge-Informed Cross-Species Foundation Model [paper]
34. [2024 Nature Methods] Toward universal cell embeddings: integrating single-cell RNA-seq datasets across species with SATURN [paper]
35. [2023 NeurIPS] MuSe-GNN: Learning Unified Gene Representation From Multimodal Biological Graph Data [paper]
36. [2023 NeurIPS 2023 AI for Science Workshop] scCLIP: Multi-modal Single-cell Contrastive Learning Integration Pre-training [paper]
37. [2023 NeurIPS 2023 AI for Science Workshop] Single-cell Masked Autoencoder: An Accurate and Interpretable Automated Immunophenotyper [paper]
38. [2023 biorxiv] scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis [paper]
39. [2023 biorxiv] Large-Scale Cell Representation Learning via Divide-and-Conquer Contrastive Learning [paper]
40. [2024 ICLR] CellPLM: Pre-training of Cell Language Model Beyond Single Cells [paper]
41. [2023 bioRxiv multi-modal] Single-cell gene expression prediction from DNA sequence at large contexts [paper]
42. [2023 bioRxiv multi-modal] Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation [paper]
43. [2023 bioRxiv] CellPolaris: Decoding Cell Fate through Generalization Transfer Learning of Gene Regulatory Networks [paper]
44. [2023 bioRxiv] scHyena: Foundation Model for Full-Length Single-Cell RNA-Seq Analysis in Brain [paper]
45. [2023 Nature Methods][scPoli] Population-level integration of single-cell datasets enables multi-scale analysis across samples [paper]
46. [2023 Nature][GeneFormer] Transfer learning enables predictions in network biology [paper]
47. [2023 iSchience][tGPT] Generative pretraining from large-scale transcriptomes for single-cell deciphering [paper]
48. [2023 NeurIPS][xTrimoGene] xTrimoGene: An Efficient and Scalable Representation Learner for Single-Cell RNA-Seq Data [paper]
49. [2022 arxiv][Exceiver] A single-cell gene expression language model [paper]
50. [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]
51. [2022 Bioinformatics][scPretrain] scPretrain: multi-task self-supervised learning for cell-type classification [paper]

Foundation-Model-Genetic-Perturbation

1. [2025 Nature Computational Science] In silico biological discovery with large perturbation models [paper]
2. [2025 Nature Biotechnology] Systema: a framework for evaluating genetic perturbation response prediction beyond systematic variation [paper]
3. [2025 biorxiv] Single Cell Foundation Models Evaluation (scFME) for In-Silico Perturbation [paper]
4. [2025 Nature Methods] Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines [paper]
5. [2025 biorxiv] Predicting cellular responses to perturbation across diverse contexts with STATE [paper]
6. [2024 biorxiv] Benchmarking a foundational cell model for post-perturbation RNAseq prediction [paper]
7. [2024 biorxiv] Benchmarking Transcriptomics Foundation Models for Perturbation Analysis: one PCA still rules them all [paper]
8. [2024 ICML] PertEval-scFM: Benchmarking Single-Cell Foundation Models for Perturbation Effect Prediction [paper]
9. [2024 BioRxiv] scGenePT: Is language all you need for modeling single-cell perturbations? [paper]
10. [2023 NM] Causal identification of single-cell experimental perturbation effects with CINEMA-OT [paper]

Foundation-Model-For-Pathology

1. [2024 bioRxiv] BiomedParse: a biomedical foundation model for image parsing of everything everywhere all at once [paper]
2. [2024 Nature] A whole-slide foundation model for digital pathology from real-world data [paper]
3. [2024 Nature Medicine FM4Pathology] Towards a general-purpose foundation model for computational pathology [paper]
4. [2024 Nature Medicine FM4Pathology] A visual-language foundation model for computational pathology [paper]
5. [2023 Nature Medicine] A visual–language foundation model for pathology image analysis using medical Twitter [paper]