Learning biological network using mutual information and conditional independence

Background: Biological networks offer us a new way to investigate the interactions among different components and address the biological system as a whole. In this paper, a reverse-phase protein microarray (RPPM) is used for the quantitative measurement of proteomic responses. Results: To discover the signaling pathway responsive to RPPM, a new structure learning algorithm of Bayesian networks is developed based on mutual Information, conditional independence, and graph immorality. Trusted biology networks are thus predicted by the new approach. As an application example, we investigate signaling networks of ataxia telangiectasis mutation (ATM). The study was carried out at different time points under different dosages for cell lines with and without gene transfection. To validate the performance ofthe proposed algorithm, comparison experiments were also implemented using three well-known networks. From the experiment results, our approach produces more reliable networks with a relati...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Dong-Chul Kim, Xiaoyu Wang, Chin-Rang Yang, Jean Gao
• Keywords: Bayesian Networks, Biological Networks, BMCBI 2010, Protein Microarray