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Foundation-Model-Evaluation-For-Single-cell

  1. [2024 Nature Methods] Assessing GPT-4 for cell type annotation in single-cell RNA-seq analysis [paper]
  2. [2024 biorxiv] Metric Mirages in Cell Embeddings [paper]
  3. [2023 biorxiv] A Deep Dive into Single-Cell RNA Sequencing Foundation Models [paper]
  4. [2023 bioRxiv scEval] Evaluating the Utilities of Large Language Models in Single-cell Data Analysis [paper]
  5. [2023 bioRxiv] Assessing the limits of zero-shot foundation models in single-cell biology [paper]
  6. [2023 bioRxiv] Foundation Models Meet Imbalanced Single-Cell Data When Learning Cell Type Annotations [paper]
  7. [2023 bioRxiv] Evaluation of large language models for discovery of gene set function [paper]
  8. [2024 ICLR benchmark DNA FD] BEND: Benchmarking DNA Language Models on Biologically Meaningful Tasks [paper]

Foundation-Model-For-Single-cell

  1. [2024 biorxiv] Nicheformer: a foundation model for single-cell and spatial omics [paper]
  2. [2024 biorxiv] Large-scale characterization of cell niches in spatial atlases using bio-inspired graph learning [paper]
  3. [2024 biorxiv] scmFormer Integrates Large-Scale Single-Cell Proteomics and Transcriptomics Data by Multi-Task Transformer [paper]
  4. [2024 biorxiv] Sequence modeling and design from molecular to genome scale with Evo [paper]
  5. [2024] Single-cell metadata as language [paper]
  6. [2023 NeurIPS] MuSe-GNN: Learning Unified Gene Representation From Multimodal Biological Graph Data [paper]
  7. [2023 biorxiv] scNODE: Generative Model for Temporal Single Cell Transcriptomic Data Prediction [paper]
  8. [2023 biorxiv] Universal Cell Embeddings: A Foundation Model for Cell Biology [paper]
  9. [2023 NeurIPS 2023 AI for Science Workshop] scCLIP: Multi-modal Single-cell Contrastive Learning Integration Pre-training [paper]
  10. [2023 NeurIPS 2023 AI for Science Workshop] Single-cell Masked Autoencoder: An Accurate and Interpretable Automated Immunophenotyper [paper]
  11. [2023 biorxiv] scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis [paper]
  12. [2023 biorxiv] Large-Scale Cell Representation Learning via Divide-and-Conquer Contrastive Learning [paper]
  13. [2023 arxiv multimodal] MuSe-GNN: Learning Unified Gene Representation From Multimodal Biological Graph Data [paper]
  14. [2023 Nature Machine Intelligence] Reusability report: Learning the transcriptional grammar in single-cell RNA-sequencing data using transformers [paper]
  15. [2023 bioRxiv] Scalable querying of human cell atlases via a foundational model reveals commonalities across fibrosis-associated macrophages [paper]
  16. [2023 bioRxiv] To Transformers and Beyond: Large Language Models for the Genome [paper]
  17. [2023 bioRxiv] A pre-trained large generative model for translating single-cell transcriptome to proteome [paper]
  18. [2023 bioRxiv] GENEPT: A SIMPLE BUT HARD-TO-BEAT FOUNDATION MODEL FOR GENES AND CELLS BUILT FROM CHATGPT [paper]
  19. [2023 bioRxiv] CellPLM: Pre-training of Cell Language Model Beyond Single Cells [paper]
  20. [2023 Nature Biotechnology multi-modal] Integration of multi-modal single-cell data [Paper]
  21. [2023 bioRxiv multi-modal] Single-cell gene expression prediction from DNA sequence at large contexts [paper]
  22. [2023 bioRxiv multi-modal] Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation [paper]
  23. [2023 bioRxiv] CellPolaris: Decoding Cell Fate through Generalization Transfer Learning of Gene Regulatory Networks [paper]
  24. [2023 bioRxiv] GeneCompass: Deciphering Universal Gene Regulatory Mechanisms with Knowledge-Informed Cross-Species Foundation Model [paper]
  25. [2023 bioRxiv] scHyena: Foundation Model for Full-Length Single-Cell RNA-Seq Analysis in Brain [paper]
  26. [2023 XXXX] A Deeper Dive into Single-Cell RNA Sequencing Foundation Models
  27. [2023 bioRxiv] GET: a foundation model of transcription across human cell types [paper]
  28. [2023 bioRxiv] Cell2Sentence: Teaching Large Language Models the Language of Biology [paper]
  29. [2023 bioRxiv][scTranslator] A pre-trained large language model for translating single-cell transcriptome to proteome [paper]
  30. [2023 bioRxiv][scPoli] Population-level integration of single-cell datasets enables multi-scale analysis across samples [paper]
  31. [2023 bioRxiv] Towards Universal Cell Embeddings: Integrating Single-cell RNA-seq Datasets across Species with SATURN [paper]
  32. [2023 bioRxiv][scFoundation] Large Scale Foundation Model on Single-cell Transcriptomics [paper]
  33. [2023 Nature][GeneFormer] Transfer learning enables predictions in network biology [paper]
  34. [2023 iSchience][tGPT] Generative pretraining from large-scale transcriptomes for single-cell deciphering [paper]
  35. [2023 bioRxiv][scGPT] scGPT: Towards Building a Foundation Model for Single-Cell Multi-omics Using Generative AI [paper v1], [paper v2]
  36. [2023 bioRxiv][xTrimoGene] xTrimoGene: An Efficient and Scalable Representation Learner for Single-Cell RNA-Seq Data [paper]
  37. [2022 arxiv][Exceiver] A single-cell gene expression language model [paper]
  38. [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]
  39. [2022 bioRxiv][scFormer] scFormer: a universal representation learning approach for single-cell data using transformers [paper]
  40. [2022 Bioinformatics][scPretrain] scPretrain: multi-task self-supervised learning for cell-type classification [paper]

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