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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Bibliographic Details
Main Authors: Louis Verny, Nadir Sella, Séverine Affeldt, Param Priya Singh, Hervé Isambert
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2017-10-01
Series:PLoS Computational Biology
Online Access:http://europepmc.org/articles/PMC5685645?pdf=render