Using Bayesian Networks for Estimating Gene Networks from Microarrays and Biological Knowledge Seiya Imoto
this paper, we provide a general framework for combining microarray data and biological knowledge, including protein-protein interactions, protein-DNA interactions, binding site information, existing literature and so on, aimed at estimating a gene network by using a Bayesian network model. Our proposed method automatically tunes the balance between the biological knowledge and microarray data based on our criterion and estimates a gene network from the combined data. We analyze Saccharomyces cerevisiae gene expression data as an application
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