Efficient selection of discriminative genes from microarray gene expression data for cancer diagnosis

Huang, D; Chow, TWS; Ma, EWM; Li, J

Efficient selection of discriminative genes from microarray gene expression data for cancer diagnosis

Huang, D Chow, TWS Ma, EWM Li, J

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Circuits and Systems I: Regular Papers, 2005, 52 (9), pp. 1909 - 1918
Issue Date:: 2005-09-01

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Field	Value	Language
dc.contributor.author	Huang, D	en_US
dc.contributor.author	Chow, TWS	en_US
dc.contributor.author	Ma, EWM	en_US
dc.contributor.author	Li, J https://orcid.org/0000-0003-1833-7413	en_US
dc.date.issued	2005-09-01	en_US
dc.identifier.citation	IEEE Transactions on Circuits and Systems I: Regular Papers, 2005, 52 (9), pp. 1909 - 1918	en_US
dc.identifier.issn	1057-7122	en_US
dc.identifier.uri	http://hdl.handle.net/10453/15249
dc.description.abstract	A new mutual information (MI)-based feature-selection method to solve the so-called large p and small n problem experienced in a microarray gene expression-based data is presented. First, a grid-based feature clustering algorithm is introduced to eliminate redundant features. A huge gene set is then greatly reduced in a very efficient way. As a result, the computational efficiency of the whole feature-selection process is substantially enhanced. Second, MI is directly estimated using quadratic MI together with Parzen window density estimators. This approach is able to deliver reliable results even when only a small pattern set is available. Also, a new MI-based criterion is proposed to avoid the highly redundant selection results in a systematic way. At last, attributed to the direct estimation of MI, the appropriate selected feature subsets can be reasonably determined. © 2005 IEEE.	en_US
dc.relation.ispartof	IEEE Transactions on Circuits and Systems I: Regular Papers	en_US
dc.relation.isbasedon	10.1109/TCSI.2005.852013	en_US
dc.subject.classification	Electrical & Electronic Engineering	en_US
dc.title	Efficient selection of discriminative genes from microarray gene expression data for cancer diagnosis	en_US
dc.type	Journal Article
utslib.citation.volume	9	en_US
utslib.citation.volume	52	en_US
utslib.for	0906 Electrical and Electronic Engineering	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/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	open_access
pubs.issue	9	en_US
pubs.publication-status	Published	en_US
pubs.volume	52	en_US

Abstract:

A new mutual information (MI)-based feature-selection method to solve the so-called large p and small n problem experienced in a microarray gene expression-based data is presented. First, a grid-based feature clustering algorithm is introduced to eliminate redundant features. A huge gene set is then greatly reduced in a very efficient way. As a result, the computational efficiency of the whole feature-selection process is substantially enhanced. Second, MI is directly estimated using quadratic MI together with Parzen window density estimators. This approach is able to deliver reliable results even when only a small pattern set is available. Also, a new MI-based criterion is proposed to avoid the highly redundant selection results in a systematic way. At last, attributed to the direct estimation of MI, the appropriate selected feature subsets can be reasonably determined. © 2005 IEEE.

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http://hdl.handle.net/10453/15249