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We consider integrative modeling of multiple gene networks and diversegenomic data, including protein-DNA binding, gene expression and DNA sequencedata, to accurately identify the regulatory target genes of a transcriptionfactor (TF). Rather than treating all the genes equally and independently apriori in existing joint modeling approaches, we incorporate the biologicalprior knowledge that neighboring genes on a gene network tend to be (or not tobe) regulated together by a TF. A key contribution of our work is that, tomaximize the use of all existing biological knowledge, we allow incorporationof multiple gene networks into joint modeling of genomic data by introducing amixture model based on the use of multiple Markov random fields (MRFs). Anotherimportant contribution of our work is to allow different genomic data to becorrelated and to examine the validity and effect of the independenceassumption as adopted in existing methods. Due to a fully Bayesian approach,inference about model parameters can be carried out based on MCMC samples.Application to an E. coli data set, together with simulation studies,demonstrates the utility and statistical efficiency gains with the proposedjoint model.

Bayesian joint modeling of multiple gene networks and diverse genomic data to identify target genes of a transcription factor