Clustering and cross-talk in a yeast functional interaction network

Jennifer Hallinan*, Anil Wipat

*Corresponding author for this work

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

3 Citations (Scopus)

Abstract

Many different clustering algorithms have been applied to biological networks, with varying degrees of success. The output of a clustering algorithm may be hard to interpret in biological terms because such networks are often large and highly interconnected, with structural and functional modules overlapping to varying degrees. In this paper we describe an evolutionary network clustering algorithm specifically designed for the analysis of large, complex biological networks. It identifies variably sized, overlapping clusters of nodes. The identification of points of overlap between clusters facilitates the analysis of the biological nature of crosstalk between functional units in the network. We apply two variants of the algorithm (one using probabilistic weights on edges and one ignoring them) to a recently published network of functional gene interactions in the yeast Saccharomyces cerevisiae and assess the biological validity of the resulting clusters in terms of ontological similarity.

Original languageEnglish
Title of host publication2006 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology
Place of PublicationPiscataway, N.J.
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages140-147
Number of pages8
ISBN (Electronic)1424406242
ISBN (Print)1424406234, 9781424406234
DOIs
Publication statusPublished - Sep 2006
Externally publishedYes
Event3rd Computational Intelligence in Bioinformatics and Computational Biology Symposium, CIBCB - Toronto, ON, Canada
Duration: 28 Sep 200629 Sep 2006

Other

Other3rd Computational Intelligence in Bioinformatics and Computational Biology Symposium, CIBCB
CountryCanada
CityToronto, ON
Period28/09/0629/09/06

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