A hybrid clustering algorithm for identifying modules in Protein Protein Interaction networks

Authors:
Liang Yu;Lin Gao;Peng Gang Sun
Affiliations:
School of Computer Science and Technology, Xidian University, Xi;an, 710071, China.;School of Computer Science and Technology, Xidian University, Xi
Venue:
International Journal of Data Mining and Bioinformatics
Year:
2010

Citing 10
Cited 3

A clustering algorithm based on graph connectivity

Information Processing Letters
Functional topology in a network of protein interactions

Bioinformatics
Protein complex prediction via cost-based clustering

Bioinformatics
Iterative Cluster Analysis of Protein Interaction Data

Bioinformatics
Effective Pre-Processing Strategies for Functional Clustering of a Protein-Protein Interactions Network

BIBE '05 Proceedings of the Fifth IEEE Symposium on Bioinformatics and Bioengineering
Detecting functional modules in the yeast protein--protein interaction network

Bioinformatics
Modular organization of protein interaction networks

Bioinformatics
Brief communication: Identification of functional modules in a PPI network by clique percolation clustering

Computational Biology and Chemistry
An ensemble framework for clustering protein–protein interaction networks

Bioinformatics
Improving functional modularity in protein-protein interactions graphs using hub-induced subgraphs

PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases

Identification of conserved protein complexes by module alignment

International Journal of Data Mining and Bioinformatics
The conformational changes analysis of Maltodextrin binding protein based on elastic network model

International Journal of Data Mining and Bioinformatics
A supervised approach to detect protein complex by combining biological and topological properties

International Journal of Data Mining and Bioinformatics

Quantified Score

Hi-index	0.00

Visualization

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.