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Harnessing the power of single-cell large language models with parameter-efficient fine-tuning using scPEFT
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Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625
Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A. C., Lo, W.-Y., et al. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00371 · Cited by 11,953 · cited 1×
“…control cells from other T cell subtypes. n = 874, 391, 242, 724, 2,174, 203, 868, 1,941 and 205 cells were used for the test cases, respectively. c – e , Histograms of differential attention scores from native scGPT ( c ), fine-tuned scGPT ( d ) and scPEFT ( e ) models, in the analysis of…”
The control of the false discovery rate in multiple testing under dependency
Benjamini, Y., & Yekutieli, D. (2001) · The Annals of Statistics · doi:10.1214/aos/1013699998 · Cited by 10,490
Santos, A. G., da Rocha, G. O., & de Andrade, J. B. (2019) · Scientific Reports · doi:10.1038/s41598-018-37186-2 · Cited by 8,495
Evolutionary-scale prediction of atomic level protein structure with a language model
Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., Verkuil, R., Kabeli, O., Shmueli, Y., et al. (2022) · doi:10.1101/2022.07.20.500902 · Cited by 5,990
From Louvain to Leiden: guaranteeing well-connected communities
Traag, V. A., Waltman, L., & van Eck, N. J. (2019) · Scientific Reports · doi:10.1038/s41598-019-41695-z · Cited by 5,635
Dictionary learning for integrative, multimodal and scalable single-cell analysis
Hao, Y., Stuart, T., Kowalski, M. H., Choudhary, S., Hoffman, P., Hartman, A., Srivastava, A., Molla, G., Madad, S., Fernandez-Granda, C., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-01767-y · Cited by 5,631
Dimensionality reduction for visualizing single-cell data using UMAP
Becht, E., McInnes, L., Healy, J., Dutertre, C.-A., Kwok, I. W. H., Ng, L. G., Ginhoux, F., & Newell, E. W. (2019) · Nature Biotechnology · doi:10.1038/nbt.4314 · Cited by 5,358
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Research works printed but not linked: 1
- Hu, E. J. et al. LoRA: Low-rank adaptation of large language models. In The 10th International Conference on Learning Representations (ICLR, 2022).
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Sparse Autoencoders Reveal Interpretable Features in Single-Cell Foundation ModelsPreprint
Pedrocchi, F., Barkmann, F., Joudaki, A., & Boeva, V. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.22.681631 · Cited by 9
Benchmarking biomedical foundation models
Sáez-Rodríguez, J., Schäfer, P. S. L., Kalavros, N., & Stolovitzky, G. A. (2026) · Nature Methods · doi:10.1038/s41592-026-03182-y · Cited by 6
Zou, X., & Lei, X. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag503
Zhao, M., Ma, X., Shi, W., & Sun, Y. (2026) · Life Sciences · doi:10.1016/j.lfs.2026.124600 · Cited by 1
Wen, J., Wang, Z., Huang, J., Li, F., Chen, N., & Ma, Y. (2026) · Advanced Science · doi:10.1002/advs.77094
Metadata
- Authors
- He, F., Fei, R., Krull, J. E., Yu, Y., Zhang, X., Wang, X., Cheng, H., Gao, M., Su, L., Chen, Y., Li, J., Jin, B., Chang, Y., Ma, A., Ma, Q., & Xu, D.
- Affiliations
- University of Missouri (US)
- The Ohio State University (US)
- Corresponding author
- Ma, Qin
- Xu, Dong
- Venue
- Nature Machine Intelligence
- Published
- 2025-12-31
- Volume
- 8
- Issue
- 1
- Pages
- 118-133
- ISSN
- 2522-5839 (ISSN-L)
- PMC
- PMC12045372
- OpenAlex
- W7117735905
- Cited by
- 5
- References
- 62
- Citations by year
- 2026: 4 · 2025: 1
- FWCI
- 1.50
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Machine Intelligence (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- Research Square (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion, cc-by)
- Funders
- Foundation for the National Institutes of Health (R35GM126985, R01GM152585, P01CA278732, P01AI177687, U54AG075931, R01DK138504)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- parameter-efficient fine-tuning, adapters, catastrophic forgetting, zero-shot prediction, single-cell atlas, interspecies transmission, cell population identification, GPU memory reduction
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