CONGO: Clustering on the Gene Ontology

Kennedy, PJ; Simoff, SJ

CONGO: Clustering on the Gene Ontology

Kennedy, PJ

Simoff, SJ

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Publisher:: University of Technology, Sydney
Publication Type:: Conference Proceeding
Citation:: Congress on Evolutionary Computation. Proceedings of the 2nd Australasian Data Mining Workshop, 2003, pp. 181 - 198
Issue Date:: 2003-01

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Field	Value	Language
dc.contributor.author	Kennedy, PJ https://orcid.org/0000-0001-7837-3171	en_US
dc.contributor.author	Simoff, SJ	en_US
dc.contributor.editor	Simoff, SJ	en_US
dc.contributor.editor	Williams, GJ	en_US
dc.contributor.editor	Hegland, M	en_US
dc.date	2003-12-08	en_US
dc.date.issued	2003-01	en_US
dc.identifier.citation	Congress on Evolutionary Computation. Proceedings of the 2nd Australasian Data Mining Workshop, 2003, pp. 181 - 198	en_US
dc.identifier.isbn	0-9751724-1-7	en_US
dc.identifier.uri	http://hdl.handle.net/10453/6936
dc.description.abstract	With the invention of microarray technology, researchers are capable of measuring the expression levels of ten thousands of genes in parallel at various time points of the biological process. During the investigation of gene regulatory networks and general cellular mechanisms, biologists are attempting to group genes based on the time-depending pattern of the obtained expression levels. In this paper, we propose a new memetic algorithm - a genetic algorithm combined with local search-based on a tree representation of the data - a minimum spanning tree minus; for clustering gene expression data. The combination of both concepts is shown to find near-optimal solutions quickly. Due to the minimum spanning tree representation of the data, our algorithm is capable of finding clusters of different shapes. We show that our approach is superior in solution quality compared to classical clustering methods.	en_US
dc.publisher	University of Technology, Sydney	en_US
dc.relation.ispartof	Congress on Evolutionary Computation. Proceedings of the 2nd Australasian Data Mining Workshop	en_US
dc.relation.ispartof	IEEE Congress on Evolutionary Computation	en_US
dc.relation.isbasedon	10.1109/CEC.2003.1299897	en_US
dc.title	CONGO: Clustering on the Gene Ontology	en_US
dc.type	Conference Proceeding
utslib.location	Sydney, Australia	en_US
utslib.location.activity	Canberra, Australia	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
dc.location.activity	Canberra, Australia	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	open_access
pubs.consider-herdc	true	en_US
pubs.place-of-publication	Sydney, Australia	en_US
pubs.start-date	2003-12-08	en_US

Abstract:

With the invention of microarray technology, researchers are capable of measuring the expression levels of ten thousands of genes in parallel at various time points of the biological process. During the investigation of gene regulatory networks and general cellular mechanisms, biologists are attempting to group genes based on the time-depending pattern of the obtained expression levels. In this paper, we propose a new memetic algorithm - a genetic algorithm combined with local search-based on a tree representation of the data - a minimum spanning tree minus; for clustering gene expression data. The combination of both concepts is shown to find near-optimal solutions quickly. Due to the minimum spanning tree representation of the data, our algorithm is capable of finding clusters of different shapes. We show that our approach is superior in solution quality compared to classical clustering methods.

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http://hdl.handle.net/10453/6936