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Open Access Highly Accessed Research article

Protein complex detection using interaction reliability assessment and weighted clustering coefficient

Nazar Zaki1*, Dmitry Efimov2 and Jose Berengueres1

Author affiliations

1 Intelligent Systems, College of Information Technology, UAEU, Al Ain, UAE

2 Faculty of Mechanics and Mathematics, Moscow State Uni., Moscow, Russia

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Citation and License

BMC Bioinformatics 2013, 14:163  doi:10.1186/1471-2105-14-163

Published: 20 May 2013

Abstract

Background

Predicting protein complexes from protein-protein interaction data is becoming a fundamental problem in computational biology. The identification and characterization of protein complexes implicated are crucial to the understanding of the molecular events under normal and abnormal physiological conditions. On the other hand, large datasets of experimentally detected protein-protein interactions were determined using High-throughput experimental techniques. However, experimental data is usually liable to contain a large number of spurious interactions. Therefore, it is essential to validate these interactions before exploiting them to predict protein complexes.

Results

In this paper, we propose a novel graph mining algorithm (PEWCC) to identify such protein complexes. Firstly, the algorithm assesses the reliability of the interaction data, then predicts protein complexes based on the concept of weighted clustering coefficient. To demonstrate the effectiveness of the proposed method, the performance of PEWCC was compared to several methods. PEWCC was able to detect more matched complexes than any of the state-of-the-art methods with higher quality scores.

Conclusions

The higher accuracy achieved by PEWCC in detecting protein complexes is a valid argument in favor of the proposed method. The datasets and programs are freely available at http://faculty.uaeu.ac.ae/nzaki/Research.htm webcite.