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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| 主要な著者: | , , , , |
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| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
Public Library of Science (PLoS)
2017-10-01
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| シリーズ: | PLoS Computational Biology |
| オンライン･アクセス: | http://europepmc.org/articles/PMC5685645?pdf=render |
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