期刊名称：International Journal of Advanced Research In Computer Science and Software Engineering
印刷版ISSN：2277-6451
电子版ISSN：2277-128X
出版年度：2012
卷号：2
期号：3
出版社：S.S. Mishra
摘要：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 com-plexity 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
