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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...
| Autores principales: | , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Public Library of Science
2017
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| Materias: | |
| 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 |