http://www.arxiv.org/abs/math.ST/0610034 Bayesian Variable Selection and Data Integration for Biological Regulatory Networks Authors: Shane T. Jensen, Guang Chen, Christian J. Stoeckert Jr Comments: Submitted to the Journal of the American Statistical Association Subj-class: Statistics; Molecular Networks A substantial focus of research in molecular biology is the network of factors which control the involvement of different genes in living cells. Previous statistical approaches for identifying gene regulatory networks have used gene expression data, ChIP binding data or promoter sequence data, but each of these resources provides only partial information. We present a Bayesian hierarchical model that integrates all three data types in a principled variable selection framework. The gene expression data is modeled as a function of the unknown gene regulatory network which has an informed prior distribution based upon both ChIP binding and promoter sequence data. We also present a variable weighting methodology for the principled balancing of multiple sources of prior information. We apply our procedure to the discovery of gene regulatory relationships in Yeast for which we can use several external sources of information to validate our results. Our inferred relationships show greater biological relevance on the external validation measures than previous data integration methods.