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dc.contributor.authorJi, Guolizh_CN
dc.contributor.authorYang, Zijiangzh_CN
dc.contributor.authorYou, Wenjiezh_CN
dc.contributor.author吉国力zh_CN
dc.date.accessioned2013-12-12T02:49:27Z
dc.date.available2013-12-12T02:49:27Z
dc.date.issued2011-09zh_CN
dc.identifier.citationIeee Transactions on Systems Man and Cybernetics Part C-Applications and Reviews, 2011,41(6):830-841zh_CN
dc.identifier.issn1094-6977zh_CN
dc.identifier.otherWOS:000296019400005zh_CN
dc.identifier.urihttps://dspace.xmu.edu.cn/handle/2288/70711
dc.descriptionNational Natural Science Foundation of China [60774033]; Special Research Fund for the Doctoral Program of Higher Education [20070384003, 20090121110022]; Xiamen University [0630-E62000]; Natural Sciences and Engineering Research Council of Canadazh_CN
dc.description.abstractIn view of the characteristics of high-dimensional small sample, strong relevance, and high noise of the identification of tumor-specific genes on microarray, a novel partial least squares (PLS) based gene-selection method, which synthesizes genetic relatedness and is suitable for multicategory classification, is presented. Using the explanation difference of independent variables on dependent variable (class), we define three indicators for global gene selection, which takes into accounts the combined effects of all the genes and the correlation among the genes. Integrated with the linear kernel support vector classifier (SVC), the proposed method is tested by MIT acute myeloid leukemia/acute lymphoblastic leukemia (AML/ALL) and small round blue cell tumors (SRBCT) data sets. A subset of specific genes with small numbers and high identification are obtained. The results indicate that our proposed PLS-based method for tumor-specific genes selection is highly efficient. Compared to the literature, the selected specific genes from both two-category dataset AML/ALL and multicategory dataset SRBCT are credible. Further investigation shows that the proposed gene-selection method is robust. Overall, the proposed method can effectively solve feature-selection problem on high-dimensional small sample. At the same time, it has good performance for multicategory classification as well.zh_CN
dc.language.isoen_USzh_CN
dc.source.urihttp://dx.doi.org/10.1109/TSMCC.2010.2078503zh_CN
dc.subjectSUPPORT VECTOR MACHINESzh_CN
dc.subjectPARTIAL LEAST-SQUARESzh_CN
dc.subjectCANCER CLASSIFICATIONzh_CN
dc.subjectMUTUAL INFORMATIONzh_CN
dc.subjectEXPRESSIONzh_CN
dc.subjectPREDICTIONzh_CN
dc.subjectREGRESSIONzh_CN
dc.subjectDIAGNOSISzh_CN
dc.titlePLS-Based Gene Selection and Identification of Tumor-Specific Geneszh_CN
dc.typeArticlezh_CN


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