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

摘要

BackgroundThe 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" analysis typically the inference of a directed graphical model is required. However, this is rather difficult due to the curse of dimensionality.