The advent of high-throughput techniques is transforming biology into a data rich field. A variety of genomics data is now available, each providing a different perspective of gene regulation. Even though each type of data requires specific computational methods, methods that combine complimentary datasets are necessary to obtain additional information that is not available by analyzing the either of the dataset alone. In this paper, we propose a Bayesian approach to integrate gene expression data with genome-wide protein-DNA interaction data. The proposed method combines these datasets in order to probabilistic predict transcription factors for genes. We evaluate the proposed method using Saccharomyces Cerevisiae Cell Cycle data. Results are compared with that of previous method. © Springer-Verlag Berlin Heidelberg 2007.