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Article: A multiple-filter-multiple-wrapper approach to gene selection and microarray data classification

TitleA multiple-filter-multiple-wrapper approach to gene selection and microarray data classification
Authors
KeywordsClassifier design and evaluation
Feature evaluation and selection
Filters
Gene selection
Hybrid classification models
Microarray data classification
Wrappers.
Issue Date2010
PublisherIEEE.
Citation
Ieee/Acm Transactions On Computational Biology And Bioinformatics, 2010, v. 7 n. 1, p. 108-117 How to Cite?
AbstractFilters and wrappers are two prevailing approaches for gene selection in microarray data analysis. Filters make use of statistical properties of each gene to represent its discriminating power between different classes. The computation is fast but the predictions are inaccurate. Wrappers make use of a chosen classifier to select genes by maximizing classification accuracy, but the computation burden is formidable. Filters and wrappers have been combined in previous studies to maximize the classification accuracy for a chosen classifier with respect to a filtered set of genes. The drawback of this single-filter-single-wrapper (SFSW) approach is that the classification accuracy is dependent on the choice of specific filter and wrapper. In this paper, a multiple-filter-multiple-wrapper (MFMW) approach is proposed that makes use of multiple filters and multiple wrappers to improve the accuracy and robustness of the classification, and to identify potential biomarker genes. Experiments based on six benchmark data sets show that the MFMW approach outperforms SFSW models (generated by all combinations of filters and wrappers used in the corresponding MFMW model) in all cases and for all six data sets. Some of MFMW-selected genes have been confirmed to be biomarkers or contribute to the development of particular cancers by other studies. © 2006 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/124745
ISSN
2015 Impact Factor: 1.609
2015 SCImago Journal Rankings: 0.706
ISI Accession Number ID
Funding AgencyGrant Number
HKU CRCG
Funding Information:

The authors would like to thank the reviewers for their valuable comments which helped improve the manuscript in many ways. This work was supported by a HKU CRCG research grant.

References

 

DC FieldValueLanguage
dc.contributor.authorLeung, Yen_HK
dc.contributor.authorHung, Yen_HK
dc.date.accessioned2010-10-31T10:51:41Z-
dc.date.available2010-10-31T10:51:41Z-
dc.date.issued2010en_HK
dc.identifier.citationIeee/Acm Transactions On Computational Biology And Bioinformatics, 2010, v. 7 n. 1, p. 108-117en_HK
dc.identifier.issn1545-5963en_HK
dc.identifier.urihttp://hdl.handle.net/10722/124745-
dc.description.abstractFilters and wrappers are two prevailing approaches for gene selection in microarray data analysis. Filters make use of statistical properties of each gene to represent its discriminating power between different classes. The computation is fast but the predictions are inaccurate. Wrappers make use of a chosen classifier to select genes by maximizing classification accuracy, but the computation burden is formidable. Filters and wrappers have been combined in previous studies to maximize the classification accuracy for a chosen classifier with respect to a filtered set of genes. The drawback of this single-filter-single-wrapper (SFSW) approach is that the classification accuracy is dependent on the choice of specific filter and wrapper. In this paper, a multiple-filter-multiple-wrapper (MFMW) approach is proposed that makes use of multiple filters and multiple wrappers to improve the accuracy and robustness of the classification, and to identify potential biomarker genes. Experiments based on six benchmark data sets show that the MFMW approach outperforms SFSW models (generated by all combinations of filters and wrappers used in the corresponding MFMW model) in all cases and for all six data sets. Some of MFMW-selected genes have been confirmed to be biomarkers or contribute to the development of particular cancers by other studies. © 2006 IEEE.en_HK
dc.languageengen_HK
dc.publisherIEEE.-
dc.relation.ispartofIEEE/ACM Transactions on Computational Biology and Bioinformaticsen_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rightsIEEE - ACM Transactions on Computational Biology and Bioinformatics. Copyright © IEEE.-
dc.rights©2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.subjectClassifier design and evaluationen_HK
dc.subjectFeature evaluation and selectionen_HK
dc.subjectFiltersen_HK
dc.subjectGene selectionen_HK
dc.subjectHybrid classification modelsen_HK
dc.subjectMicroarray data classificationen_HK
dc.subjectWrappers.en_HK
dc.titleA multiple-filter-multiple-wrapper approach to gene selection and microarray data classificationen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1545-5963&volume=7&issue=1&spage=108&epage=117&date=2010&atitle=A+multiple-filter-multiple-wrapper+approach+to+gene+selection+and+microarray+data+classification-
dc.identifier.emailHung, Y:yshung@eee.hku.hken_HK
dc.identifier.authorityHung, Y=rp00220en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/TCBB.2008.46en_HK
dc.identifier.pmid20150673en_HK
dc.identifier.scopuseid_2-s2.0-76849096874en_HK
dc.identifier.hkuros175047en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-76849096874&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume7en_HK
dc.identifier.issue1en_HK
dc.identifier.spage108en_HK
dc.identifier.epage117en_HK
dc.identifier.isiWOS:000274063600010-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridLeung, Y=35490882300en_HK
dc.identifier.scopusauthoridHung, Y=8091656200en_HK

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