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From correlation to causation networks: a simple approximate learning algorithm and its application to high-dimensional plant gene expression data

BACKGROUND: The use of correlation networks is widespread in the analysis of gene expression and proteomics data, even though it is known that correlations not only confound direct and indirect associations but also provide no means to distinguish between cause and effect. For "causal" ana...

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Detalles Bibliográficos
Autores principales: Opgen-Rhein, Rainer, Strimmer, Korbinian
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2007
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1995222/
https://www.ncbi.nlm.nih.gov/pubmed/17683609
http://dx.doi.org/10.1186/1752-0509-1-37