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...
Main Authors: | , , , , |
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Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2017-10-01
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Series: | PLoS Computational Biology |
Online Access: | http://europepmc.org/articles/PMC5685645?pdf=render |