Performance Comparison of the Structure Learning Bayesian Network Algorithms Using Gene Expression Data

Understanding gene interactions in complex living systems can be seen as the ultimate goal of the systems biology revolution. Hence, to fully understand disease ontology and to reduce the cost of drug development, gene regulatory networks (GRN) have to be constructed. During the last decade, many GRN inference algorithms like ‘Bayesian network’ that are based on genome-wide data have been developed to unravel the complexity of gene regulation. Recently, many of structure learning algorithms were used to learn Bayesian network that have shown promise in gene regulatory network reconstruction. In this paper we apply different structure learning algorithms on actual microarray data to obtain a better understanding of their relative strengths and weaknesses on the system biology community and we evaluate their outputs from different perspectives.