BAYESIAN VARIABLE SELECTION AND DATA INTEGRATION FOR BIOLOGICAL REGULATORY NETWORKS

A substantial focus of research in molecular biology are gene regulatory networks: the set of transcription factors and target genes which control C the involvement of different biological processes in living cells. Previous statistical approaches for identifying gene regulatory networks have Used 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 are modeled as a function of the Unknown gene regulatory network which has all informed prior distribution based Upon both Chip binding and promoter sequence data. We also present a variable weighing, methodology for the principled balancing of multiple S our procedure 10 the discovery of sources of prior information. We apply gene regulatory relationships in Saccharomyces cerevisiae (Yeast) for which we call 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. Our model also estimates synergistic and antagonistic interactions between transcription factors many of which are validated by previous studies. We also evaluate the results from our procedures for the weighting for multiple sources of prior information. Finally, we discuss our methodology in the context of previous approaches to data integration and Bayesian variable selection.