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 language | English |
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Title of host publication | 2006 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology |
Place of Publication | Piscataway, N.J. |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 140-147 |
Number of pages | 8 |
ISBN (Electronic) | 1424406242 |
ISBN (Print) | 1424406234, 9781424406234 |
DOIs | |
Publication status | Published - Sept 2006 |
Externally published | Yes |
Event | 3rd Computational Intelligence in Bioinformatics and Computational Biology Symposium, CIBCB - Toronto, ON, Canada Duration: 28 Sept 2006 → 29 Sept 2006 |
Other
Other | 3rd Computational Intelligence in Bioinformatics and Computational Biology Symposium, CIBCB |
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Country/Territory | Canada |
City | Toronto, ON |
Period | 28/09/06 → 29/09/06 |