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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., DOI: 10.1038/s41588-025-02124-2 (2025)
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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Publication type Article: Journal article
Document type Review
Corresponding Author
ISSN (print) / ISBN 1061-4036
e-ISSN 1546-1718
Journal Nature Genetics
Publisher Nature Publishing Group
Publishing Place New York, NY
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)
Helmholtz Artifical Intelligence Cooperation Unit (HAICU)