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This article is part of the supplement: Selected articles from the 2009 IEEE International Conference on Bioinformatics and Biomedicine

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Learning biological network using mutual information and conditional independence

Dong-Chul Kim1, Xiaoyu Wang2, Chin-Rang Yang2 and Jean Gao1*

  • * Corresponding author: Jean Gao

  • † Equal contributors

Author Affiliations

1 Department of Computer Science and Engineering The University of Texas at Arlington Arlington, TX, 76019, USA

2 Simmons Comprehensive Cancer Center, The University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA

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BMC Bioinformatics 2010, 11(Suppl 3):S9  doi:10.1186/1471-2105-11-S3-S9

Published: 29 April 2010



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.


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 relatively small number of wrong connection especially in mid-size networks. By using the proposed method, we predicted different networks for ATM under different doses of radiation treatment, and those networks were compared with results from eight different protein protein interaction (PPI) databases.


By using a new protein microarray technology in combination with a new computational framework, we demonstrate an application of the methodology to the study of biological networks of ATM cell lines under low dose ionization radiation.