Gene Network Modeling through Semi-Fixed Bayesian Network

Gene networks describe functional pathways in a given cell or tissue, representing processes such as metabolism, gene expression regulation, protein or RNA transport. Thus, learning gene network is a crucial problem in the post genome era. Most existing works learn gene networks by assuming one gene provokes the expression of another gene directly leading to an over-simplified model. In this paper, we show that the gene regulation is a complex problem with many hidden variables. We propose a semi-fixed model to represent the gene network as a Bayesian network with hidden variables. In addition, an effective algorithm to learn the model is presented. Experiments on artificial and real-life dataset confirm the effectiveness of our approach.