Construction of gene networks with hybrid approach from expression profile and gene ontology

Gene regulatory networks have been long studied in model organisms as a means of identifying functional relationships among genes or their corresponding products. Despite many existing methods for genome-wide construction of such networks, solutions to the gene regulatory networks problem are however not trivial. Here, we present, a hybrid approach with gene expression profiles and gene ontology (HAEO). HAEO makes use of multimethods (overlapping clustering and reverse engineering methods) to effectively and efficiently construct gene regulatory networks from multisources (gene expression profiles and gene ontology). Application to yeast cell cycle dataset demonstrates HAEO's ability to construct validated gene regulatory networks, such as some potential gene regulatory pairs, which cannot be discovered by general inferring methods and identifying cycles (i.e., feedback loops) between genes. We also experimentally study the efficiency of building networks and show that the proposed method, HAEO is much faster than Bayesian networks method.