Title: An integrated approach to identify protein complex based on best neighbour and modularity increment

Authors: Xianjun Shen; Yanli Zhao; Yanan Li; Yang Yi; Tingting He; Jincai Yang

Addresses: School of Computer, Central China Normal University, Wuhan, China ' School of Computer, Central China Normal University, Wuhan, China ' School of Computer, Central China Normal University, Wuhan, China ' School of Computer, Central China Normal University, Wuhan, China ' School of Computer, Central China Normal University, Wuhan, China ' School of Computer, Central China Normal University, Wuhan, China

Abstract: In order to overcome the limitations of global modularity and the deficiency of local modularity, we propose a hybrid modularity measure Local-Global Quantification (LGQ) which considers global modularity and local modularity together. LGQ adopts a suitable module feature adjustable parameter to control the balance of global detecting capability and local search capability in Protein-Protein Interactions (PPI) Network. Furthermore, we develop a new protein complex mining algorithm called Best Neighbour and Local-Global Quantification (BN-LGQ) which integrates the best neighbour node and modularity increment. BN-LGQ expands the protein complex by fast searching the best neighbour node of the current cluster and by calculating the modularity increment as a metric to determine whether the best neighbour node can join the current cluster. The experimental results show BN-LGQ performs a better accuracy on predicting protein complexes and has a higher match with the reference protein complexes than MCL and MCODE algorithms. Moreover, BN-LGQ can effectively discover protein complexes with better biological significance in the PPI network.

Keywords: protein complexes; best neighbour node; modularity increment; protein-protein interaction networks; PPI networks; bioinformatics; global modularity; local modularity; data mining.

DOI: 10.1504/IJDMB.2015.067973

International Journal of Data Mining and Bioinformatics, 2015 Vol.11 No.4, pp.458 - 473

Received: 14 Mar 2014
Accepted: 22 Mar 2014
Published online: 12 Mar 2015 *

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