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Article: Gene selection for brain cancer classification

TitleGene selection for brain cancer classification
Authors
Issue Date2006
Citation
Conference Proceedings : ... Annual International Conference Of The Ieee Engineering In Medicine And Biology Society. Ieee Engineering In Medicine And Biology Society. Conference, 2006, v. 1, p. 5846-5849 How to Cite?
AbstractWith the introduction of microarray, cancer classification, diagnosis and prediction are made more accurate and effective. However, the final outcome of the data analyses very much depend on the huge number of genes with relatively small number of samples present in each experiment. It is thus crucial to select relevant genes to be used for future specific cancer markers. Many feature selection methods have been proposed but none is able to classify all kinds of microarray data accurately, especially on those multi-class datasets. We propose a one-versus-one comparison method for selecting discriminatory features instead of performing the statistical test in a one-versus-all manner. Brain cancer is chosen as an example. Here, 3 types of statistics are used: signal-to-noise ratio (SNR), t-statistics and Pearson correlation coefficient. Results are verified by performing hierarchical and k-means clustering. Using our one-versus-one comparisons, best performance accuracies of 90.48% and 97.62% can be obtained by hierarchical and k-means clustering respectively. However best performance accuracies of 88.10% and 80.95% can be obtained respectively when using one-versus-all comparison. This shows that one-versus-one comparison is superior.
Persistent Identifierhttp://hdl.handle.net/10722/155438
ISSN

 

DC FieldValueLanguage
dc.contributor.authorLeung, YYen_HK
dc.contributor.authorChang, CQen_HK
dc.contributor.authorHung, YSen_HK
dc.contributor.authorFung, PCen_HK
dc.date.accessioned2012-08-08T08:33:29Z-
dc.date.available2012-08-08T08:33:29Z-
dc.date.issued2006en_HK
dc.identifier.citationConference Proceedings : ... Annual International Conference Of The Ieee Engineering In Medicine And Biology Society. Ieee Engineering In Medicine And Biology Society. Conference, 2006, v. 1, p. 5846-5849en_HK
dc.identifier.issn1557-170Xen_HK
dc.identifier.urihttp://hdl.handle.net/10722/155438-
dc.description.abstractWith the introduction of microarray, cancer classification, diagnosis and prediction are made more accurate and effective. However, the final outcome of the data analyses very much depend on the huge number of genes with relatively small number of samples present in each experiment. It is thus crucial to select relevant genes to be used for future specific cancer markers. Many feature selection methods have been proposed but none is able to classify all kinds of microarray data accurately, especially on those multi-class datasets. We propose a one-versus-one comparison method for selecting discriminatory features instead of performing the statistical test in a one-versus-all manner. Brain cancer is chosen as an example. Here, 3 types of statistics are used: signal-to-noise ratio (SNR), t-statistics and Pearson correlation coefficient. Results are verified by performing hierarchical and k-means clustering. Using our one-versus-one comparisons, best performance accuracies of 90.48% and 97.62% can be obtained by hierarchical and k-means clustering respectively. However best performance accuracies of 88.10% and 80.95% can be obtained respectively when using one-versus-all comparison. This shows that one-versus-one comparison is superior.en_HK
dc.languageengen_US
dc.relation.ispartofConference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conferenceen_HK
dc.subject.meshAlgorithmsen_US
dc.subject.meshBrain Neoplasms - Diagnosis - Geneticsen_US
dc.subject.meshCluster Analysisen_US
dc.subject.meshComputational Biology - Methodsen_US
dc.subject.meshDiagnosis, Computer-Assisteden_US
dc.subject.meshGene Expression Profilingen_US
dc.subject.meshGene Expression Regulation, Neoplasticen_US
dc.subject.meshHumansen_US
dc.subject.meshModels, Statisticalen_US
dc.subject.meshModels, Theoreticalen_US
dc.subject.meshNeoplasm Proteins - Metabolismen_US
dc.subject.meshOligonucleotide Array Sequence Analysisen_US
dc.subject.meshPattern Recognition, Automateden_US
dc.subject.meshTumor Markers, Biological - Metabolismen_US
dc.titleGene selection for brain cancer classificationen_HK
dc.typeArticleen_HK
dc.identifier.emailChang, CQ: cqchang@eee.hku.hken_HK
dc.identifier.emailHung, YS: yshung@hkucc.hku.hken_HK
dc.identifier.authorityChang, CQ=rp00095en_HK
dc.identifier.authorityHung, YS=rp00220en_HK
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1109/IEMBS.2006.260197-
dc.identifier.pmid17947170en_HK
dc.identifier.scopuseid_2-s2.0-34047115780en_HK
dc.identifier.hkuros131504-
dc.identifier.volume1en_HK
dc.identifier.spage5846en_HK
dc.identifier.epage5849en_HK
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridLeung, YY=35490882300en_HK
dc.identifier.scopusauthoridChang, CQ=7407033052en_HK
dc.identifier.scopusauthoridHung, YS=8091656200en_HK
dc.identifier.scopusauthoridFung, PC=7101613315en_HK

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