Bayesian Joint Modeling of Multiple Gene Networks and Diverse Genomic Data to Identify Target Genes of a Transcription Factor

• Activity Number: 609
• Type: Topic Contributed
• Date/Time: Thursday, August 5, 2010 : 8:30 AM to 10:20 AM
• Sponsor: ENAR
• Author(s): Peng Wei*+ and Wei Pan
• Companies: The University of Texas School of Public Health and University of Minnesota
• Keywords: Bayesian hierarchical modeling ; gene networks ; joint modeling ; Markov random field ; systems biology

Abstract

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 by a TF together. To maximize the use of all existing biological knowledge, we allow the incorporation of multiple gene networks into joint modeling of genomic data by introducing a Markov random field-based model. Application to an E. coli dataset together with simulation studies demonstrates the utility and statistical efficiency gains of the proposed joint model.