Clustering incorporating shortest paths identifies relevant modules in functional interaction networks

Jennifer Hallinan*, Matthew Pocock, Stephen Addinall, David A. Lydall, Anil Wipat

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

Abstract

Many biological systems can be modeled as networks. Hence, network analysis is of increasing importance to systems biology. We describe an evolutionary algorithm for selecting clusters of nodes within a large network based upon network topology together with a measure of the relevance of nodes to a set of independently identified genes of interest. We apply the algorithm to a previously published integrated functional network of yeast genes, using a set of query genes derived from a whole genome screen of yeast strains with a mutation in a telomere uncapping gene. We find that the algorithm identifies biologically plausible clusters of genes which are related to the cell cycle, and which contain interactions not previously identified as potentially important. We conclude that the algorithm is valuable for the querying of complex networks, and the generation of biological hypotheses.

Original languageEnglish
Title of host publication2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2009 - Proceedings
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages228-236
Number of pages9
ISBN (Print)9781424427567
DOIs
Publication statusPublished - Apr 2009
Externally publishedYes
Event2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB - 2009 - Nashville, United States
Duration: 30 Mar 20092 Apr 2009

Other

Other2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB - 2009
CountryUnited States
CityNashville
Period30/03/092/04/09

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