Abstract:In light of the low signal-to-noise nature of many large biological data sets, we propose a novel method to identify the structure of association networks using a Gaussian graphical model combined with prior knowledge. Our algorithm includes the following two parts. In the first part we propose a model selection criterion called structural Bayesian information criterion (SBIC) in which the prior structure is modeled and incorporated into the Bayesian information criterion (BIC). It is shown that the popular ex… Show more
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