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dc.contributor.authorDağlıyan, Onur
dc.contributor.authorÜney-Yüksektepe, Fadime
dc.contributor.authorKavaklı, Halil
dc.contributor.authorTürkay, Metin
dc.date.accessioned2016-09-07T13:00:20Z
dc.date.available2016-09-07T13:00:20Z
dc.date.issued2011-02-04
dc.identifier.issn1932-6203
dc.identifier.urihttp://hdl.handle.net/11413/1441
dc.description.abstractBackground: An important use of data obtained from microarray measurements is the classification of tumor types with respect to genes that are either up or down regulated in specific cancer types. A number of algorithms have been proposed to obtain such classifications. These algorithms usually require parameter optimization to obtain accurate results depending on the type of data. Additionally, it is highly critical to find an optimal set of markers among those up or down regulated genes that can be clinically utilized to build assays for the diagnosis or to follow progression of specific cancer types. In this paper, we employ a mixed integer programming based classification algorithm named hyper-box enclosure method (HBE) for the classification of some cancer types with a minimal set of predictor genes. This optimization based method which is a user friendly and efficient classifier may allow the clinicians to diagnose and follow progression of certain cancer types. Methodology/Principal Findings: We apply HBE algorithm to some well known data sets such as leukemia, prostate cancer, diffuse large B-cell lymphoma (DLBCL), small round blue cell tumors (SRBCT) to find some predictor genes that can be utilized for diagnosis and prognosis in a robust manner with a high accuracy. Our approach does not require any modification or parameter optimization for each data set. Additionally, information gain attribute evaluator, relief attribute evaluator and correlation-based feature selection methods are employed for the gene selection. The results are compared with those from other studies and biological roles of selected genes in corresponding cancer type are described. Conclusions/Significance: The performance of our algorithm overall was better than the other algorithms reported in the literature and classifiers found in WEKA data-mining package. Since it does not require a parameter optimization and it performs consistently very high prediction rate on different type of data sets, HBE method is an effective and consistent tool for cancer type prediction with a small number of gene markers.tr_TR
dc.language.isoen_UStr_TR
dc.publisherPublic Library Science, 185 Berry St, Ste 1300, San Francisco, Ca 94107 USAtr_TR
dc.relationPlos Onetr_TR
dc.subjectBayesian Variable Selectiontr_TR
dc.subjectPartial Least-squarestr_TR
dc.subjectB-cell lymphomastr_TR
dc.subjectProstate-Cancertr_TR
dc.subjectLogistic-Regressiontr_TR
dc.subjectPredictiontr_TR
dc.subjectLeukemiatr_TR
dc.subjectBindingtr_TR
dc.subjectIdentificationtr_TR
dc.subjectOrganizationtr_TR
dc.subjectBayesci Değişken Seçimitr_TR
dc.subjectKısmi En Küçük Karelertr_TR
dc.subjectB-hücreli Lenfomalartr_TR
dc.subjectProstat Kanseritr_TR
dc.subjectLojistik Regresyontr_TR
dc.subjectTahmintr_TR
dc.subjectLösemitr_TR
dc.subjectBağlayıcıtr_TR
dc.subjectKimliktr_TR
dc.subjectOrganizasyontr_TR
dc.titleOptimization Based Tumor Classification From Microarray Gene Expression Datatr_TR
dc.typeArticletr_TR
dc.contributor.authorIDTR108243tr_TR
dc.contributor.authorIDTR40319tr_TR
dc.contributor.authorIDTR24956tr_TR
dc.identifier.wos287037000002
dc.identifier.scopus2-s2.0-79951526058


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