Clustering in a fixed manifold to detect groups of genes with similar expression patterns

Phuong, NM; Tuan, HD

Clustering in a fixed manifold to detect groups of genes with similar expression patterns

Phuong, NM Tuan, HD

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Publication Type:: Conference Proceeding
Citation:: Communications in Computer and Information Science, 2008, 13 pp. 32 - 42
Issue Date:: 2008-01-01

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Field	Value	Language
dc.contributor.author	Phuong, NM	en_US
dc.contributor.author	Tuan, HD https://orcid.org/0000-0003-0292-6061	en_US
dc.date.issued	2008-01-01	en_US
dc.identifier.citation	Communications in Computer and Information Science, 2008, 13 pp. 32 - 42	en_US
dc.identifier.isbn	9783540705987	en_US
dc.identifier.issn	1865-0929	en_US
dc.identifier.uri	http://hdl.handle.net/10453/16454
dc.description.abstract	Clustering genes into groups that exhibit similar expression patterns is one of the most fundamental issues in microarray data analysis. In this paper, we present a normalized Expectation-Maximization (EM) approach for the problem of gene-based clustering. The normalized EMclustering also follows the framework of generative clustering models but for the data in a fixed manifold. We illustrate the effectiveness of the normalized EM on two real microarray data sets by comparing its clustering results with the ones produced by other related clustering algorithms. It is shown that the normalized EM performs better than the related algorithms in term of clustering outcomes. © Springer-Verlag Berlin Heidelberg 2008.	en_US
dc.relation.ispartof	Communications in Computer and Information Science	en_US
dc.title	Clustering in a fixed manifold to detect groups of genes with similar expression patterns	en_US
dc.type	Conference Proceeding
utslib.citation.volume	13	en_US
utslib.for	1004 Medical Biotechnology	en_US
dc.location.activity	Vienna, Austria	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 Electrical and Data Engineering
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	13	en_US

Abstract:

Clustering genes into groups that exhibit similar expression patterns is one of the most fundamental issues in microarray data analysis. In this paper, we present a normalized Expectation-Maximization (EM) approach for the problem of gene-based clustering. The normalized EMclustering also follows the framework of generative clustering models but for the data in a fixed manifold. We illustrate the effectiveness of the normalized EM on two real microarray data sets by comparing its clustering results with the ones produced by other related clustering algorithms. It is shown that the normalized EM performs better than the related algorithms in term of clustering outcomes. © Springer-Verlag Berlin Heidelberg 2008.

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http://hdl.handle.net/10453/16454