Title: A hybrid clustering algorithm for identifying modules in Protein–Protein Interaction networks

Authors: Liang Yu, Lin Gao, Peng Gang Sun

Addresses: School of Computer Science and Technology, Xidian University, Xi'an, 710071, China. ' School of Computer Science and Technology, Xidian University, Xi'an, 710071, China. ' School of Computer Science and Technology, Xidian University, Xi'an, 710071, China

Abstract: Identifying modules in Protein–Protein Interaction (PPI) networks is important to understand the organisation of the cellular processes. In this paper, we present a novel algorithm combining Molecular Complex Detection (MCODE) with Girvan–Newman (GN) to identify modules in PPI networks. Our algorithm can accurately discover denser modules in large-scale protein interaction networks. We applied it to S. cerevisiae PPI networks and obtained high matching rate between the predicted modules and the known protein complexes in Munich Information Center for Protein Sequences (MIPS). The simulation results show that our algorithm provides an effective, reliable and scalable method of identifying modules in PPI networks.

Keywords: protein–protein interaction; PPI networks; graph clustering; functional modules; protein complexes; bioinformatics; modules; molecular complex detection; protein sequences.

DOI: 10.1504/IJDMB.2010.035903

International Journal of Data Mining and Bioinformatics, 2010 Vol.4 No.5, pp.600 - 615

Published online: 08 Oct 2010 *

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