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A parallel algorithm for clustering protein-protein interaction networks

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2 Author(s)
Q. Yang ; Dept. of Comput. Sci., California Univ., Riverside, CA, USA ; S. Lonardi

The increasing availability of interaction graphs requires new resource-efficient tools capable of extracting valuable biological knowledge from these networks. In this paper we report on a novel parallel implementation of Girvan and Newman's clustering algorithm that is capable of running on clusters of computers. Our parallel implementation achieves almost linear speed-up up to 32 processors and allows us to run this computationally intensive algorithm on large protein-protein interaction networks. Preliminary experiments show that the algorithm has very high accuracy in identifying functional related protein modules.

Published in:

2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05)

Date of Conference:

8-11 Aug. 2005