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dc.contributor.authorNg, Shu-Kayen_US
dc.contributor.authorJ.. McLachlan, Geoffreyen_US
dc.contributor.editorGuo-Zheng Li, Xiaohua Hu, Sunghoon Kim, Habtom Ressomen_US
dc.date.accessioned2017-05-03T13:14:16Z
dc.date.available2017-05-03T13:14:16Z
dc.date.issued2013en_US
dc.date.modified2014-05-30T03:20:20Z
dc.identifier.refurihttp://bibm2013.tongji.edu.cn/en_US
dc.identifier.doi10.1109/BIBM.2013.6732501en_US
dc.identifier.urihttp://hdl.handle.net/10072/59674
dc.description.abstractThe identification of genes that have different expression levels in a known number of distinct disease phenotypes contributes significantly to the construction of a discriminant rule (classifier) for predicting the class of origin of an unclassified tissue sample. Existing methods for detecting differentially-expressed genes are mainly based on multiple hypothesis tests. Clustering-based approaches either work on gene-specific summary statistics or reduced forms of gene-expression profiles. Advancement in clustering-based approaches that work on full profiling data has been minor, due to the methodological barriers for assessing differential expression between tissue classes from identified clusters of genes. In this paper, we adopt a clustering-based approach, which works on full gene-expression profiles and draws inference on differential expression using weighted contrasts of mixed effects. With a real published gene-expression data set, we show that the proposed clustering-based approach can provide a list of marker genes that improves the prediction of disease outcomes. Comparisons with existing methods are also provided using simulated data.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_US
dc.languageEnglishen_US
dc.publisherIEEEen_US
dc.publisher.placeUnited Statesen_US
dc.relation.ispartofstudentpublicationNen_US
dc.relation.ispartofconferencenameBIBM 2013en_US
dc.relation.ispartofconferencetitleProceedings - 2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013en_US
dc.relation.ispartofdatefrom2013-12-18en_US
dc.relation.ispartofdateto2013-12-21en_US
dc.relation.ispartoflocationShanghai, Chinaen_US
dc.rights.retentionYen_US
dc.subject.fieldofresearchcode270203en_US
dc.titleUsing cluster analysis to improve gene selection in the formation of discriminant rules for the prediction of disease outcomesen_US
dc.typeConference outputen_US
dc.type.descriptionE1 - Conference Publications (HERDC)en_US
dc.type.codeE - Conference Publicationsen_US
gro.facultyGriffith Health, School of Medicineen_US
gro.hasfulltextNo Full Text


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    Contains papers delivered by Griffith authors at national and international conferences.

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