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2013 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2013)
Shanghai, China
Dec. 18, 2013 to Dec. 21, 2013
ISBN: 978-1-4799-1309-1
pp: 29-36
Yuan Zhang , College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, 100124, China
Nan Du , Department of Computer Science and Engineering, State University of New York at Buffalo, Buffalo, NY 14260, USA
Kang Li , Department of Computer Science and Engineering, State University of New York at Buffalo, Buffalo, NY 14260, USA
Jinchao Feng , College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, 100124, China
Kebin Jia , College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, 100124, China
Aidong Zhang , Department of Computer Science and Engineering, State University of New York at Buffalo, Buffalo, NY 14260, USA
ABSTRACT
Critical node detection in dynamic networks is of great value in many areas, such as the evolving of friendship in social networks, the development of epidemics, molecular pathogenesis of diseases and so on. As for detecting critical nodes in dynamic Protein-Protein Interaction Networks (PPINs), there are mainly two challenges: the first is to construct the dynamic PPINs that are not available directly from biological experiments in laboratories; and the second is how to identify the most critical units that are responsible for the dynamic processes. This paper proposes effective framework to tackle these two problems. First of all, this paper proposes to construct the dynamic PPINs by simultaneously modeling the activity of proteins and assembling the dynamic co-regulation protein network at each time point. As result, more comprehensive dynamic PPINs are built. Besides, a novel critical protein detection method that integrates multiple PPI networks into a Deep Belief Network model (referred to as MIDBN) is developed. The integrated model is trained to get hierarchical common representations of multiple sources which are used to reconstruct the original data. The variabilities of the reconstruction errors across the time courses are ranked to finally get the top proteins that have significantly different evolving structural patterns than the other nodes in the dynamic networks. We evaluated our network construction method by comparing the functional representations of the derived networks with that of two other traditional construction methods, and our method achieved superior function analysis results. The ranking results of critical proteins from MIDBN were compared with results from two baseline methods and the comparison results showed that MIDBN had better reconstruction rate and identified more proteins of critical value to yeast cell cycle process.
INDEX TERMS
Proteins, Gene expression, Correlation, Joints, Vectors, Diseases, Data models
CITATION

Y. Zhang, N. Du, K. Li, J. Feng, K. Jia and A. Zhang, "Critical protein detection in dynamic PPI networks with multi-source integrated deep belief nets," 2013 IEEE International Conference on Bioinformatics and Biomedicine(BIBM), Shanghai, China China, 2013, pp. 29-36.
doi:10.1109/BIBM.2013.6732606
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