  We consider integrative modeling of multiple gene networks and diverse
genomic data, including protein-DNA binding, gene expression and DNA sequence
data, to accurately identify the regulatory target genes of a transcription
factor (TF). Rather than treating all the genes equally and independently a
priori in existing joint modeling approaches, we incorporate the biological
prior knowledge that neighboring genes on a gene network tend to be (or not to
be) regulated together by a TF. A key contribution of our work is that, to
maximize the use of all existing biological knowledge, we allow incorporation
of multiple gene networks into joint modeling of genomic data by introducing a
mixture model based on the use of multiple Markov random fields (MRFs). Another
important contribution of our work is to allow different genomic data to be
correlated and to examine the validity and effect of the independence
assumption 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 proposed
joint model.
