Gene Network Learning Using Regulated Dynamic Bayesian Network Methods

Dynamic Bayesian network (DBN) methods have shown great promise in regulatory network reconstruction because of their capability of modeling causality and cyclic networks, and handling data with noises found in biological experiments. However, they tend to produce relative high false positives and are not computationally efficient even for networks of moderate size. This paper presents a novel DBN-based approach to address these issues. For each node, a differential mutual information is used to select potential parents and a Bayesian scoring metric with a Dirichlet prior for regulation is applied to evaluate its parents. The proposed method is applied to recover a network structure from simulated data with higher accuracy and computational efficiency compared to DBNs with other scoring metrics. When applied to infer a cell cycle pathway of Saccharomyces cerevisiae using real time-series expression data, the proposed method is capable of identifying most gene interactions in the pathways.