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Open Access Research article

RRW: repeated random walks on genome-scale protein networks for local cluster discovery

Kathy Macropol1*, Tolga Can2 and Ambuj K Singh1

Author Affiliations

1 Department of Computer Science, University of California, Santa Barbara, CA 93106, USA

2 Department of Computer Engineering, Middle East Technical University, 06531 Ankara, Turkey

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BMC Bioinformatics 2009, 10:283  doi:10.1186/1471-2105-10-283

Published: 9 September 2009

Abstract

Background

We propose an efficient and biologically sensitive algorithm based on repeated random walks (RRW) for discovering functional modules, e.g., complexes and pathways, within large-scale protein networks. Compared to existing cluster identification techniques, RRW implicitly makes use of network topology, edge weights, and long range interactions between proteins.

Results

We apply the proposed technique on a functional network of yeast genes and accurately identify statistically significant clusters of proteins. We validate the biological significance of the results using known complexes in the MIPS complex catalogue database and well-characterized biological processes. We find that 90% of the created clusters have the majority of their catalogued proteins belonging to the same MIPS complex, and about 80% have the majority of their proteins involved in the same biological process. We compare our method to various other clustering techniques, such as the Markov Clustering Algorithm (MCL), and find a significant improvement in the RRW clusters' precision and accuracy values.

Conclusion

RRW, which is a technique that exploits the topology of the network, is more precise and robust in finding local clusters. In addition, it has the added flexibility of being able to find multi-functional proteins by allowing overlapping clusters.