Inference of gene regulatory network based on module network model with gene functional classifications

We propose a novel method for an exhaustive inference of gene regulatory networks from genome-wide expression data and biological knowledge. Our method performs the inferences based on module network model. In the model a module is a set of genes with similar features, and a network represents regulatory relationships among the modules. Our method makes modules using gene functional classification together with expression data. We apply our method to inferences of the networks of yeast cell-cycle. Modules inferred by our method show consistency with experimentally-determined results on yeast cell-cycle, especially on G1 phase. Robust modules built by our method permit us to infer informative regulatory relationships.