Large-scale causal discovery using interventional data sheds light on the regulatory network architecture of blood traits.

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Brown BC, Morris JA, Lappalainen T, Knowles DA

bioRxiv - (-) - [2023-10-17; online 2023-10-17]

Inference of directed biological networks is an important but notoriously challenging problem. We introduce inverse sparse regression (inspre), an approach to learning causal networks that leverages large-scale intervention-response data. Applied to 788 genes from the genome-wide perturb-seq dataset, inspre helps elucidate the network architecture of blood traits.

PubMed 37905013

DOI 10.1101/2023.10.13.562293

Crossref 10.1101/2023.10.13.562293

pmc: PMC10614812
pii: 2023.10.13.562293