A fast agglomerative community detection method for protein complex discovery in protein interaction networks

Authors:
Mohammad S. Rahman;Alioune Ngom
Affiliations:
School of Computer Sciences, University of Windsor, Windsor, Ontario, Canada;School of Computer Sciences, University of Windsor, Windsor, Ontario, Canada
Venue:
PRIB'13 Proceedings of the 8th IAPR international conference on Pattern Recognition in Bioinformatics
Year:
2013

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Abstract

Proteins are known to interact with each other by forming protein complexes and in order to perform specific biological functions. Many community detection methods have been devised for the discovery of protein complexes in protein interaction networks. One common problem in current agglomerative community detection approaches is that vertices with just one neighbor are often classified as separate clusters, which does not make sense for complex identification. Also, a major limitation of agglomerative techniques is that their computational efficiency do not scale well to large protein interaction networks (PINs). In this paper, we propose a new agglomerative algorithm, FAC-PIN, based on a local premetric of relative vertex-to-vertex clustering value and which addresses the above two issues. Our proposed FAC-PIN method is applied to eight PINs from different species, and the identified complexes are validated using experimentally verified complexes. The preliminary computational results show that FAC-PIN can discover protein complexes from PINs more accurately and faster than the HC-PIN and CNM algorithms, the current state-of-the-art agglomerative approaches to complex prediction.