A scalable approach for inferring transcriptional regulation in the yeast cell cycle

The high complexity in the gene regulation mechanism and the prevalent noise in high-throughput detection experiments are considered to be the two major obstacles in discovering transcriptional regulation with high accuracy from experimental gene expression data. In this paper, we study a model based on dynamic Bayesian networks to predict gene regulation by integrating transcription factor binding site data and proteinprotein interaction data with gene expression data. The knowledge of genetic interactions between proteins and the presence of transcription factors binding site at the promoter region of a gene have been used to restrict the number of potential regulators of each gene. We show the effectiveness of combining multiple data sources in the prediction of transcriptional regulation through the analysis of Saccharomyces cerevisiae (Yeast) cell cycle data. Experiments conducted on real microarray datasets show that the proposed model is significantly more efficient and topologically more accurate compared to other existing models based on dynamic Bayesian networks. We also demonstrate the scalability of the proposed model through the analysis of a large dataset with a sustainable performance level.