Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/38131
DC FieldValueLanguage
dc.contributor.authorLai, C.H.en_US
dc.contributor.author蔡孟勳zh_TW
dc.contributor.authorYu, S.S.en_US
dc.contributor.authorChang, J.D.en_US
dc.contributor.authorTsai, M.H.en_US
dc.contributor.author喻石生zh_TW
dc.date2009zh_TW
dc.date.accessioned2014-06-06T08:00:32Z-
dc.date.available2014-06-06T08:00:32Z-
dc.identifier.issn1349-4198zh_TW
dc.identifier.urihttp://hdl.handle.net/11455/38131-
dc.description.abstractIn this paper, the human ovarian cDNA expression database is analyzed for discriminating oncogenes according to different pathological stages of ovarian carcinoma. The human ovarian cDNA expression database of this paper collects 41 patient samples, which includes 13 samples at normal ovarian tumors, 6 samples of borderline of cancers, 7 samples of ovarian cancer at stage I and 15 samples of ovarian cancer at stage III. Due to 9,600 genes of each pathological sample, a large number of genes are analyzed and discovered difficulty. For this reason, linear regression and analysis of variance (ANOVA) are used to discover and detect 21 notable oncogenes. Further more, these 21 notable oncogenes are divided and examined by support vector machine (SVM) with 5 different classifications according to their gene expressions of pathological stages. From the experimental results, the average accuracy of 5 classification experiments is 89% in cross validation. Moreover, the related scientific literatures also indicate these 21 discovered oncogenes are related to ovarian cancer or other cancers. It proves these discovered oncogenes are highly related to different cancers. Finally, this paper also develops a graphical user interface (GUI) bio-statistical system for gene expression analysis to assist doctors and pathologists to analyze and diagnose ovarian cancer.en_US
dc.language.isoen_USzh_TW
dc.relationInternational Journal of Innovative Computing Information and Controlen_US
dc.relation.ispartofseriesInternational Journal of Innovative Computing Information and Control, Volume 5, Issue 10A, Page(s) 3157-3177.en_US
dc.subjectOvarian canceren_US
dc.subjectOncogenesen_US
dc.subjectGene expression analysisen_US
dc.subjectMicroarrayen_US
dc.subjectdatabaseen_US
dc.subjectSupport vector machineen_US
dc.subjectbreast-cancer cellsen_US
dc.subjectsupport vector machineen_US
dc.subjectgene-expressionen_US
dc.subjecttranscription factoren_US
dc.subjectprostate-canceren_US
dc.subjectprotein expressionen_US
dc.subjectmolecularen_US
dc.subjectmarkersen_US
dc.subjecttumor progressionen_US
dc.subjectepithelial-cellsen_US
dc.subjecthe-4 wfdc2en_US
dc.titleSTATISTICAL AND SVM-BASED ONCOGENE DETECTION OF HUMAN CDNA EXPRESSIONS FOR OVARIAN CARCINOMAen_US
dc.typeJournal Articlezh_TW
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.languageiso639-1en_US-
item.openairetypeJournal Article-
item.grantfulltextnone-
item.fulltextno fulltext-
item.cerifentitytypePublications-
crisitem.author.dept資訊科學與工程學系所-
crisitem.author.parentorg理學院-
Appears in Collections:資訊科學與工程學系所
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