Information enhanced model selection for Gaussian graphical model with application to metabolomic data
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In light of the low signal-to-noise nature of many large biological data sets, we propose a novel method to learn the structure of association networks using Gaussian graphical models combined with prior knowledge. Our strategy includes two parts. In the first... ...
