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Toward learning a foundational representation of cells and genes

Nature Methods volume 21, pages 1416–1417 (2024)Cite this article

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Inspired by the success of large-scale machine learning models in natural language, several groups are adapting these models for cellular data using massive single-cell datasets.

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Fig. 1: Schema of the architecture and tasks of scRNA-seq foundation models.

References

  1. Cui, H. et al. Nat. Methods https://doi.org/10.1038/s41592-024-02201-0 (2024).

    Article  PubMed  Google Scholar 

  2. Hao, M. et al. Nat. Methods https://doi.org/10.1038/s41592-024-02305-7 (2024).

    Article  PubMed  Google Scholar 

  3. Hou, W. & Ji, Z. Nat. Methods https://doi.org/10.1038/s41592-024-02235-4 (2024).

    Article  PubMed  Google Scholar 

  4. OpenAI. GPT-4 Technical Report. OpenAI https://cdn.openai.com/papers/gpt-4.pdf (2023).

  5. Yang, F. et al. Nat. Mach. Intell. 4, 852–866 (2022).

    Article  Google Scholar 

  6. Theodoris, C. V. et al. Nature 618, 616–624 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Lotfollahi, M. et al. Mol. Syst. Biol. 19, e11517 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Lotfollahi, M., Wolf, F. A. & Theis, F. J. Nat. Methods 16, 715–721 (2019).

    Article  CAS  PubMed  Google Scholar 

  9. Kedzierska, K. Z., Crawford, L., Amini, A. P. & Lu, A. X. Preprint at bioRxiv https://doi.org/10.1101/2023.10.16.561085 (2023).

  10. Luecken, M. D. et al. Nat. Methods 19, 41–50 (2022).

    Article  CAS  PubMed  Google Scholar 

  11. Boiarsky, R., Singh, N., Buendia, A., Getz, G. & Sontag, D. Preprint at bioRxiv https://doi.org/10.1101/2023.10.19.563100 (2023).

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Authors and Affiliations

  1. Wellcome Trust Sanger Institute, Cambridge, UK

    Mohammad Lotfollahi

  2. Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK

    Mohammad Lotfollahi

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  1. Mohammad Lotfollahi
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Correspondence to Mohammad Lotfollahi.

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Competing interests

M.L. consults for Santa Anna Bio, owns interests in Relation Therapeutics and is a scientific cofounder and part-time employee at AIVIVO.

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Lotfollahi, M. Toward learning a foundational representation of cells and genes. Nat Methods 21, 1416–1417 (2024). https://doi.org/10.1038/s41592-024-02367-7

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