Learning causal networks with latent variables from multivariate information in genomic data

Learning causal networks from large-scale genomic data remains challenging in absence of time series or controlled perturbation experiments. We report an information- theoretic method which learns a large class of causal or non-causal graphical models from purely observational data, while including...

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Detalles Bibliográficos
Autores principales: Verny, Louis, Sella, Nadir, Affeldt, Séverine, Singh, Param Priya, Isambert, Hervé
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5685645/
https://www.ncbi.nlm.nih.gov/pubmed/28968390
http://dx.doi.org/10.1371/journal.pcbi.1005662

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5685645/
https://www.ncbi.nlm.nih.gov/pubmed/28968390
http://dx.doi.org/10.1371/journal.pcbi.1005662

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