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Tejada Lapuerta, A. ; Bertin, P.* ; Bauer, S. ; Aliee, H.* ; Bengio, Y.* ; Theis, F.J.

Causal machine learning for single-cell genomics.

Nat. Genet. 57, 797–808 (2025)
Verlagsversion Postprint DOI PMC
Open Access Green
Advances in single-cell '-omics' allow unprecedented insights into the transcriptional profiles of individual cells and, when combined with large-scale perturbation screens, enable measuring of the effect of targeted perturbations on the whole transcriptome. These advances provide an opportunity to better understand the causative role of genes in complex biological processes. In this Perspective, we delineate the application of causal machine learning to single-cell genomics and its associated challenges. We first present the causal model that is most commonly applied to single-cell biology and then identify and discuss potential approaches to three open problems: the lack of generalization of models to novel experimental conditions, the complexity of interpreting learned models, and the difficulty of learning cell dynamics.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Review
Sprache englisch
Veröffentlichungsjahr 2025
HGF-Berichtsjahr 2025
ISSN (print) / ISBN 1061-4036
e-ISSN 1546-1718
Zeitschrift Nature Genetics
Quellenangaben Band: 57, Heft: , Seiten: 797–808 Artikelnummer: , Supplement: ,
Verlag Nature Publishing Group
Verlagsort New York, NY
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-503800-001
G-530003-001
DOI 10.1038/s41588-025-02124-2
Scopus ID 105001874737
PubMed ID 40164735
Erfassungsdatum 2025-04-02
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