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590 related items for PubMed ID: 25549938

  • 1. A fast gene selection method for multi-cancer classification using multiple support vector data description.
    Cao J, Zhang L, Wang B, Li F, Yang J.
    J Biomed Inform; 2015 Feb; 53():381-9. PubMed ID: 25549938
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  • 2. Recursive gene selection based on maximum margin criterion: a comparison with SVM-RFE.
    Niijima S, Kuhara S.
    BMC Bioinformatics; 2006 Dec 25; 7():543. PubMed ID: 17187691
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  • 3. Development of two-stage SVM-RFE gene selection strategy for microarray expression data analysis.
    Tang Y, Zhang YQ, Huang Z.
    IEEE/ACM Trans Comput Biol Bioinform; 2007 Dec 25; 4(3):365-81. PubMed ID: 17666757
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  • 8. A Bayesian approach to joint feature selection and classifier design.
    Krishnapuram B, Hartemink AJ, Carin L, Figueiredo MA.
    IEEE Trans Pattern Anal Mach Intell; 2004 Sep 25; 26(9):1105-11. PubMed ID: 15742887
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  • 10. The feature selection bias problem in relation to high-dimensional gene data.
    Krawczuk J, Łukaszuk T.
    Artif Intell Med; 2016 Jan 25; 66():63-71. PubMed ID: 26674595
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  • 12. A multiple kernel support vector machine scheme for feature selection and rule extraction from gene expression data of cancer tissue.
    Chen Z, Li J, Wei L.
    Artif Intell Med; 2007 Oct 25; 41(2):161-75. PubMed ID: 17851055
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  • 14. Tumor classification ranking from microarray data.
    Hewett R, Kijsanayothin P.
    BMC Genomics; 2008 Sep 16; 9 Suppl 2(Suppl 2):S21. PubMed ID: 18831787
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  • 18. Reliable classification of two-class cancer data using evolutionary algorithms.
    Deb K, Raji Reddy A.
    Biosystems; 2003 Nov 16; 72(1-2):111-29. PubMed ID: 14642662
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  • 20. Applications of support vector machines to cancer classification with microarray data.
    Chu F, Wang L.
    Int J Neural Syst; 2005 Dec 16; 15(6):475-84. PubMed ID: 16385636
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