Clifti-GPT: privacy-preserving federated fine-tuning and transferable inference of foundation models on clinical single-cell data
- DOI
- 10.1186/s13040-026-00582-w
- Published
- 2026-08-05
- Container
- BioData Mining
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1186/s13040-026-00582-w,
title = {Clifti-GPT: privacy-preserving federated fine-tuning and transferable inference of foundation models on clinical single-cell data},
author = {Mohammad Bakhtiari and Maria Louise Elkjaer and Ali Oğuz Can and Fabian Theis and Mhaned Oubounyt and Jan Baumbach},
year = {2026},
journal = {BioData Mining},
doi = {10.1186/s13040-026-00582-w},
url = {https://doi.org/10.1186/s13040-026-00582-w}
}RIS
TY - JOUR TI - Clifti-GPT: privacy-preserving federated fine-tuning and transferable inference of foundation models on clinical single-cell data AU - Mohammad Bakhtiari AU - Maria Louise Elkjaer AU - Ali Oğuz Can AU - Fabian Theis AU - Mhaned Oubounyt AU - Jan Baumbach PY - 2026 JO - BioData Mining DO - 10.1186/s13040-026-00582-w UR - https://doi.org/10.1186/s13040-026-00582-w ER -
APA
Bakhtiari, M., Elkjaer, M. L., Can, A. O., Theis, F., Oubounyt, M., & Baumbach, J. (2026). Clifti-GPT: privacy-preserving federated fine-tuning and transferable inference of foundation models on clinical single-cell data. BioData Mining. https://doi.org/10.1186/s13040-026-00582-w
Source records
- crossref · retrieved 2026-11-10T14:49:17.242Z