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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References
Cui, H. et al. Nat. Methods https://doi.org/10.1038/s41592-024-02201-0 (2024).
Hao, M. et al. Nat. Methods https://doi.org/10.1038/s41592-024-02305-7 (2024).
Hou, W. & Ji, Z. Nat. Methods https://doi.org/10.1038/s41592-024-02235-4 (2024).
OpenAI. GPT-4 Technical Report. OpenAI https://cdn.openai.com/papers/gpt-4.pdf (2023).
Yang, F. et al. Nat. Mach. Intell. 4, 852–866 (2022).
Theodoris, C. V. et al. Nature 618, 616–624 (2023).
Lotfollahi, M. et al. Mol. Syst. Biol. 19, e11517 (2023).
Lotfollahi, M., Wolf, F. A. & Theis, F. J. Nat. Methods 16, 715–721 (2019).
Kedzierska, K. Z., Crawford, L., Amini, A. P. & Lu, A. X. Preprint at bioRxiv https://doi.org/10.1101/2023.10.16.561085 (2023).
Luecken, M. D. et al. Nat. Methods 19, 41–50 (2022).
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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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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DOI: https://doi.org/10.1038/s41592-024-02367-7
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