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2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2014)
Belfast, United Kingdom
Nov. 2, 2014 to Nov. 5, 2014
ISBN: 978-1-4799-5669-2
pp: 8-15
Yang Guo , Department of Computer Science and Technology, School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi, 710129, P. R. China
Xuequn Shang , Department of Computer Science and Technology, School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi, 710129, P. R. China
Qingping Zhu , Department of Computer Science and Technology, School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi, 710129, P. R. China
Mingkui Huang , Department of Computer Science and Technology, School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi, 710129, P. R. China
Zhanhuai Li , Department of Computer Science and Technology, School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi, 710129, P. R. China
ABSTRACT
Mining the protein complexes and functional modules from protein-protein interaction (PPI) networks is vital to understand the mechanism of cellular components and protein functions. Most of the proposed methods had solely focused on static properties of the PPI networks since the available PPI data are static. However, cellular systems are highly dynamic. That is, the interactions of proteins are responsive to environmental cues to accomplish diverse cellular functions. It is important to consider the dynamic inherent within the PPI networks to identify protein complexes and functional modules. In addition, most computational methods did not distinguish between protein complexes and functional modules. It is important to distinguish between them since they are different protein organizations. In this paper, we propose a novel framework to analyze the PPI networks in dynamic conditions by integrating time-series gene expression profiles data and subcelluar localization data. The algorithm, CBMI, is developed to identify protein complexes in integrated PPI networks. By investigating multiple perspectives of proteins in the PPI networks, we identify the “dynamic” hubs in the PPI networks, and then present a new method to discover the functional modules in the PPI networks. The experimental results show that the integration of temporal gene expression data and subcelluar localization data with PPI data contributes to extracting the protein complexes more precisely. Comprehensive evaluations based on f-measure and functional annotations in MIPS database reveal that our algorithm, CBMI, outperforms other previous algorithms in identifying protein complexes, and the detected functional modules are statistically significant in terms of functional annotations. The proposed framework provides a new clue to distinguish between protein complexes and functional modules, and the developed algorithms can be an effective technique for the identification of them.
INDEX TERMS
Proteins, Heuristic algorithms, Gene expression, Clustering algorithms, Protein engineering, Sensitivity
CITATION

Y. Guo, X. Shang, Q. Zhu, M. Huang and Z. Li, "Identification of protein complexes and functional modules in integrated PPI networks," 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Belfast, United Kingdom, 2014, pp. 8-15.
doi:10.1109/BIBM.2014.6999291
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