A hybrid clustering algorithm for identifying modules in Protein?Protein Interaction networks. (11th October 2010)

• Record Type: Journal Article
• Authors: Yu, Liang; Gao, Lin; Sun, Peng Gang
• Is Part Of: International journal of data mining and bioinformatics. Volume 4:Number 5(2010)
• Page Start: 600
• Page End: 615
• Publication Date: 2010-10-11
• Subjects: protein?protein interaction -- PPI networks -- graph clustering -- functional modules -- protein complexes -- bioinformatics -- modules -- molecular complex detection -- protein sequences; Data mining -- Periodicals; Bioinformatics -- Periodicals; 006.312
• Languages: English
• ISSNs: 1748-5673

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.