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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...
| Autores principales: | , |
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| Formato: | Texto |
| Lenguaje: | English |
| Publicado: |
BioMed Central
2007
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| 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 |