Large-scale causal discovery using interventional data sheds light on the regulatory network architecture of blood traits
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Large-scale causal discovery using interventional data sheds light on the regulatory network architecture of blood traits
Brown, B. C.; Morris, J. A.; Lappalainen, T.; Knowles, D. A.
AbstractInference 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.
