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Open Access Highly Accessed Research article

Protein complex prediction based on k-connected subgraphs in protein interaction network

Mahnaz Habibi1, Changiz Eslahchi1* and Limsoon Wong2*

Author Affiliations

1 Faculty of Mathematics, Shahid-Beheshti University, g.c., Tehran, Iran

2 School of Computing, National University of Singapore, Singapore

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BMC Systems Biology 2010, 4:129  doi:10.1186/1752-0509-4-129

Published: 16 September 2010



Protein complexes play an important role in cellular mechanisms. Recently, several methods have been presented to predict protein complexes in a protein interaction network. In these methods, a protein complex is predicted as a dense subgraph of protein interactions. However, interactions data are incomplete and a protein complex does not have to be a complete or dense subgraph.


We propose a more appropriate protein complex prediction method, CFA, that is based on connectivity number on subgraphs. We evaluate CFA using several protein interaction networks on reference protein complexes in two benchmark data sets (MIPS and Aloy), containing 1142 and 61 known complexes respectively. We compare CFA to some existing protein complex prediction methods (CMC, MCL, PCP and RNSC) in terms of recall and precision. We show that CFA predicts more complexes correctly at a competitive level of precision.


Many real complexes with different connectivity level in protein interaction network can be predicted based on connectivity number. Our CFA program and results are freely available from webcite.