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BioMed Research International
Volume 2014 (2014), Article ID 138410, 10 pages
Research Article

msiDBN: A Method of Identifying Critical Proteins in Dynamic PPI Networks

1College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China
2Department of Computer Science and Engineering, State University of New York at Buffalo, Buffalo, NY 14260, USA

Received 29 January 2014; Accepted 9 March 2014; Published 2 April 2014

Academic Editor: FangXiang Wu

Copyright © 2014 Yuan Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Dynamics of protein-protein interactions (PPIs) reveals the recondite principles of biological processes inside a cell. Shown in a wealth of study, just a small group of proteins, rather than the majority, play more essential roles at crucial points of biological processes. This present work focuses on identifying these critical proteins exhibiting dramatic structural changes in dynamic PPI networks. First, a comprehensive way of modeling the dynamic PPIs is presented which simultaneously analyzes the activity of proteins and assembles the dynamic coregulation correlation between proteins at each time point. Second, a novel method is proposed, named msiDBN, which models a common representation of multiple PPI networks using a deep belief network framework and analyzes the reconstruction errors and the variabilities across the time courses in the biological process. Experiments were implemented on data of yeast cell cycles. We evaluated our network construction method by comparing the functional representations of the derived networks with two other traditional construction methods. The ranking results of critical proteins in msiDBN were compared with the results from the baseline methods. The results of comparison showed that msiDBN had better reconstruction rate and identified more proteins of critical value to yeast cell cycle process.