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Volumn , Issue , 2004, Pages 413-422

The effect of noisy bootstrapping on the robustness of supervised classification of gene expression data

Author keywords

[No Author keywords available]

Indexed keywords

DNA SEQUENCES; EVOLUTIONARY ALGORITHMS; GENES; LEARNING SYSTEMS; MATHEMATICAL MODELS; ROBUSTNESS (CONTROL SYSTEMS);

EID: 17644384109     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (4)

References (13)
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    • Comparison of discrimination methods for the classification of tumor using gene expression data
    • Department of Statistics, University of California, Berkley
    • S. Dudoit, J. Fridlyand and T. Speed, "Comparison of discrimination methods for the classification of tumor using gene expression data," Techn. Report 576, Department of Statistics, University of California, Berkley, 2000.
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    • Dudoit, S.1    Fridlyand, J.2    Speed, T.3
  • 9
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    • The use of multiple measurements in taxonomic problems
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    • Fisher, R.1
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    • Support vector machine classification and validation of cancer tissue samples using microarray expression data
    • T. Furey, N. Cristianini, N. Duffy, D. Bednarski, M. Schummer and D. Haussler, "Support vector machine classification and validation of cancer tissue samples using microarray expression data," Bioinformatics, vol. 16, pp. 906-914, 2000.
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.