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Volumn 19, Issue SUPPL. 2, 2003, Pages

Discovery of significant rules for classifying cancer diagnosis data

Author keywords

[No Author keywords available]

Indexed keywords

CANCER CLASSIFICATION; CANCER DIAGNOSIS; CHILDHOOD LEUKEMIA; CONFERENCE PAPER; DIAGNOSTIC ACCURACY; DISEASE COURSE; DNA MICROARRAY; GENE EXPRESSION PROFILING; GENETIC ANALYSIS; HUMAN; LUNG CANCER; MATHEMATICAL ANALYSIS; OVARY TUMOR; PRACTICE GUIDELINE; PREDICTION; PRIORITY JOURNAL; PROGNOSIS; PROTEOMICS; SIGNAL NOISE RATIO;

EID: 7244225752     PISSN: 13674803     EISSN: 13674811     Source Type: Journal    
DOI: 10.1093/bioinformatics/btg1066     Document Type: Conference Paper
Times cited : (104)

References (20)
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    • An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization
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    • (2000) Machine Learning , vol.40 , pp. 139-158
    • Dietterich, T.G.1
  • 13
    • 0037245821 scopus 로고    scopus 로고
    • Simple rules underlying gene expression profiles of more than six subtypes of acute lymphoblastic leukemia (ALL) patients
    • Li, J., Liu, H., Downing, J.R., Yeoh, A.E.-J. and Wong, L. (2003) Simple rules underlying gene expression profiles of more than six subtypes of acute lymphoblastic leukemia (ALL) patients. Bioinformatics, 19, 71-78.
    • (2003) Bioinformatics , vol.19 , pp. 71-78
    • Li, J.1    Liu, H.2    Downing, J.R.3    Yeoh, A.E.-J.4    Wong, L.5
  • 15
    • 0025389210 scopus 로고
    • Boolean feature discovery in empirical learning
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    • Pagallo, G.1    Haussler, D.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.