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Journal of Biomedicine and Biotechnology
Volume 2011, Article ID 480294, 14 pages
Research Article

HKC: An Algorithm to Predict Protein Complexes in Protein-Protein Interaction Networks

1Institute of Mechanical Engineering and Automation, National University of Defense Technology, Changsha 410073, China
2Department of Software Engineering, Jiangnan Institute of Computing Technology, Wuxi 214083, China
3School of Computer, National University of Defense Technology, Changsha 410073, China

Received 23 May 2011; Accepted 24 August 2011

Academic Editor: Paul W. Doetsch

Copyright © 2011 Xiaomin Wang 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.


With the availability of more and more genome-scale protein-protein interaction (PPI) networks, research interests gradually shift to Systematic Analysis on these large data sets. A key topic is to predict protein complexes in PPI networks by identifying clusters that are densely connected within themselves but sparsely connected with the rest of the network. In this paper, we present a new topology-based algorithm, HKC, to detect protein complexes in genome-scale PPI networks. HKC mainly uses the concepts of highest k-core and cohesion to predict protein complexes by identifying overlapping clusters. The experiments on two data sets and two benchmarks show that our algorithm has relatively high F-measure and exhibits better performance compared with some other methods.