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

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...

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Autores principales: Brown, Brielin C., Morris, John A., Lappalainen, Tuuli, Knowles, David A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10614812/
https://www.ncbi.nlm.nih.gov/pubmed/37905013
http://dx.doi.org/10.1101/2023.10.13.562293
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author Brown, Brielin C.
Morris, John A.
Lappalainen, Tuuli
Knowles, David A.
author_facet Brown, Brielin C.
Morris, John A.
Lappalainen, Tuuli
Knowles, David A.
author_sort Brown, Brielin C.
collection PubMed
description 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.
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spelling pubmed-106148122023-10-31 Large-scale causal discovery using interventional data sheds light on the regulatory network architecture of blood traits Brown, Brielin C. Morris, John A. Lappalainen, Tuuli Knowles, David A. bioRxiv Article 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. Cold Spring Harbor Laboratory 2023-10-17 /pmc/articles/PMC10614812/ /pubmed/37905013 http://dx.doi.org/10.1101/2023.10.13.562293 Text en https://creativecommons.org/licenses/by-nd/4.0/This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nd/4.0/) , which allows reusers to copy and distribute the material in any medium or format in unadapted form only, and only so long as attribution is given to the creator. The license allows for commercial use.
spellingShingle Article
Brown, Brielin C.
Morris, John A.
Lappalainen, Tuuli
Knowles, David A.
Large-scale causal discovery using interventional data sheds light on the regulatory network architecture of blood traits
title Large-scale causal discovery using interventional data sheds light on the regulatory network architecture of blood traits
title_full Large-scale causal discovery using interventional data sheds light on the regulatory network architecture of blood traits
title_fullStr Large-scale causal discovery using interventional data sheds light on the regulatory network architecture of blood traits
title_full_unstemmed Large-scale causal discovery using interventional data sheds light on the regulatory network architecture of blood traits
title_short Large-scale causal discovery using interventional data sheds light on the regulatory network architecture of blood traits
title_sort large-scale causal discovery using interventional data sheds light on the regulatory network architecture of blood traits
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10614812/
https://www.ncbi.nlm.nih.gov/pubmed/37905013
http://dx.doi.org/10.1101/2023.10.13.562293
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