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Disease Prediction Using Ranks of Gene Expressions

DC Field Value Language
dc.contributor.author정현철-
dc.contributor.author김기열-
dc.contributor.author라선영-
dc.date.accessioned2015-05-19T17:32:40Z-
dc.date.available2015-05-19T17:32:40Z-
dc.date.issued2008-
dc.identifier.issn1598-866X-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/108345-
dc.description.abstractA large number of studies have been performed to identify biomarkers that will allow efficient detection and determination of the precise status of a patient's disease. The use of microarrays to assess biomarker status is expected to improve prediction accuracies, because a whole-genome approach is used. Despite their potential, however, patient samples can differ with respect to biomarker status when analyzed on different platforms, making it more difficult to make accurate predictions, because bias may exist between any two different experimental conditions. Because of this difficulty in experimental standardization of microarray data, it is currently difficult to utilize microarray-based gene sets in the clinic. To address this problem, we propose a method that predicts disease status using gene expression data that are transformed by their ranks, a concept that is easily applied to two datasets that are obtained using different experimental platforms. NCI and colon cancer datasets, which were assessed using both Affymetrix and cDNA microarray platforms, were used for method validation. Our results demonstrate that the proposed method is able to achieve good predictive performance for datasets that are obtained under different experimental conditions.-
dc.description.statementOfResponsibilityopen-
dc.format.extent136~141-
dc.relation.isPartOfGenomics & Informatics-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/2.0/kr/-
dc.titleDisease Prediction Using Ranks of Gene Expressions-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Internal Medicine (내과학)-
dc.contributor.googleauthorKi-Yeol Kim-
dc.contributor.googleauthorDong Hyuk Ki-
dc.contributor.googleauthorHyun Cheol Chung-
dc.contributor.googleauthorSun Young Rha-
dc.admin.authorfalse-
dc.admin.mappingfalse-
dc.contributor.localIdA03773-
dc.contributor.localIdA00337-
dc.contributor.localIdA01316-
dc.relation.journalcodeJ00940-
dc.identifier.pmidbiomarker ; different platform ; microarray ; gene expression ; rank ; prediction-
dc.subject.keywordbiomarker-
dc.subject.keyworddifferent platform-
dc.subject.keywordmicroarray-
dc.subject.keywordgene expression-
dc.subject.keywordrank-
dc.subject.keywordprediction-
dc.contributor.alternativeNameChung, Hyun Cheol-
dc.contributor.alternativeNameKim, Ki Yeol-
dc.contributor.alternativeNameRha, Sun Young-
dc.contributor.affiliatedAuthorChung, Hyun Cheol-
dc.contributor.affiliatedAuthorKim, Ki Yeol-
dc.contributor.affiliatedAuthorRha, Sun Young-
dc.rights.accessRightsfree-
dc.citation.volume6-
dc.citation.number3-
dc.citation.startPage136-
dc.citation.endPage141-
dc.identifier.bibliographicCitationGenomics & Informatics, Vol.6(3) : 136-141, 2008-
dc.identifier.rimsid35501-
dc.type.rimsART-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
2. College of Dentistry (치과대학) > Others (기타) > 1. Journal Papers

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