Reconstruction of Large-Scale Gene Regulatory Networks Using Bayesian Model Averaging

Gene regulatory networks can provide the systematic view of a complex living system. However, constructing large-scale gene regulatory networks is one of the challenging problems in Systems biology. Also a burst of various biological data demands a proper integration technique for reliable gene regulatory network construction. Here we propose a new re- verse engineering approach based on Bayesian model averaging technique which ensembles all appropriate regression models. This Bayesian approach with a prior having Gibbs distribution provides a convenient way to integrate multiple sources of biological data. In the simulation study with maximum 2000 genes, our method shows better sensitivities than elastic-net and Gaussian graphical models with a fixed specificity 0.99 though the computation time is not as good as the others. Three large-scale networks consisting of 4257 genes are built using the gene expression data of non-tumor, grade III tumor, and grade IV tumor samples, respectively. We found that genes having a large variance of degree distribution among the three tumor networks are mostly involved in regulatory and immunology of biological processes, which can provide different information from that of a differentially expressed gene analysis.