{"entity": "publication", "iuid": "46eb83b0aa1744ce892ee89d152d457f", "timestamp": "2026-09-07T11:54:51.750Z", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/46eb83b0aa1744ce892ee89d152d457f.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/46eb83b0aa1744ce892ee89d152d457f"}}, "title": "Large-scale causal discovery using interventional data sheds light on the regulatory network architecture of blood traits.", "authors": [{"family": "Brown", "given": "Brielin C", "initials": "BC"}, {"family": "Morris", "given": "John A", "initials": "JA"}, {"family": "Lappalainen", "given": "Tuuli", "initials": "T"}, {"family": "Knowles", "given": "David A", "initials": "DA"}], "type": "journal article", "published": "2023-10-17", "journal": {"title": "bioRxiv", "issn": "2692-8205", "issn-l": null}, "abstract": "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.", "doi": "10.1101/2023.10.13.562293", "pmid": "37905013", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC10614812"}, {"db": "pii", "key": "2023.10.13.562293"}], "notes": [], "created": "2026-08-20T10:48:28.311Z", "modified": "2026-08-20T10:48:28.363Z"}