A Structure Learning Algorithm for Inference of Gene Networks from Microarray Gene Expression Data Using Bayesian Networks

Estimation of gene networks based on microarray gene expression data is an important problem in systems biology. In this paper we use Bayesian networks as a mathematical model for reverse-engineering gene networks from microarray data. In such a case, structural learning of Bayesian networks is known as an NP-hard problem and we need to use heuristic algorithms to find better network structures. Recently, several algorithms have been proposed to estimate optimal Bayesian network structure, but the number of genes included in the network is limited less than 30 or so. In order to apply Bayesian network approach to drug target gene discovery, we need to consider gene networks with several hundreds of genes. Therefore we need to develop more efficient algorithms to learn Bayesian network structure based on observed data. In this paper we propose an efficient structural learning algorithm for Bayesian networks by extending K2 algorithm that is one of the standard learning algorithms in Bayesian networks. We conduct Monte Carlo simulations to examine the effectiveness of the proposed algorithm by comparing with greedy hill-climbing algorithm. We also show the application of yeast gene network estimation based on the proposed algorithm.