Identifying key factors in cell fate decisions by machine learning interpretable strategies
Author:
Publisher
Springer Science and Business Media LLC
Subject
Cell Biology,Molecular Biology,Atomic and Molecular Physics, and Optics,Biophysics
Link
https://link.springer.com/content/pdf/10.1007/s10867-023-09640-4.pdf
Reference50 articles.
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3. Bocci, F., Zhou, P., Nie, Q.: spliceJAC: transition genes and state-specific gene regulation from single-cell transcriptome data. Mol. Syst. Biol. 18(11), 11176 (2022)
4. Su, K., Katebi, A., Lu, M., et al.: NetAct: a computational platform to construct core transcription factor regulatory networks using gene activity. Genome Biol. 23(1), 1–21 (2022)
5. Trapnell, C., Cacchiarelli, D., Rinn, J.L., et al.: The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32(4), 381–386 (2014)
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