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Complexity
Volume 2017 (2017), Article ID 4120506, 11 pages
https://doi.org/10.1155/2017/4120506
Research Article

Predicting Protein Complexes in Weighted Dynamic PPI Networks Based on ICSC

1School of Computer Science, Shaanxi Normal University, Xi’an, Shaanxi 710119, China
2School of Mathematical Sciences, Nankai University, Tianjin 300071, China
3Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK, Canada S7N 5A9

Correspondence should be addressed to Xiujuan Lei; nc.ude.unns@ieljx and Fang-Xiang Wu; ac.ksasu.liam@143waf

Received 31 March 2017; Accepted 2 July 2017; Published 28 August 2017

Academic Editor: Juan A. Almendral

Copyright © 2017 Jie Zhao 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.

Abstract

Protein complexes play a critical role in understanding the biological processes and the functions of cellular mechanisms. Most existing protein complex detection algorithms cannot reflect dynamics of protein complexes. In this paper, a novel algorithm named Improved Cuckoo Search Clustering (ICSC) algorithm is proposed to detect protein complexes in weighted dynamic protein-protein interaction (PPI) networks. First, we constructed weighted dynamic PPI networks and detected protein complex cores in each dynamic subnetwork. Then, ICSC algorithm was used to cluster the protein attachments to the cores. The experimental results on both DIP dataset and Krogan dataset demonstrated that ICSC algorithm is more effective in identifying protein complexes than other competing methods.