A BAYESIAN NETWORK-BASED APPROACH TO CONSTRUCTING GENE REGULATORY NETWORK

Gene regulatory networks regulate the synthesis of essence proteins involved in the key biological processes in living cells. Many methods have been advanced to reconstruct the network structure from gene expression data, such as, Boolean network models and linear models. Bayesian network (BN) models have shown the great promise in gene regulatory network reconstruction because of their capability of capturing the casual relationships between genes and handling data with noises produced in biological experiments. In this article, we first processed the data by the hierarchical clustering analysis and the inter-information entropy based analysis to classify the genes to many gene clusters. Then, we chose some gene clusters to construct the gene regulatory network and analyzed the gene network to get the bio-medical information.