Optimal aggregation of binary classifiers for multiclass cancer diagnosis using gene expression profiles

IEEE/ACM Transactions on Computational Biology and Bioinformatics

Volumn 6, Issue 2, 2009, Pages 333-343

  Yukinawa, Naoto ^a   Oba, Shigeyuki ^a   Kato, Kikuya ^b   Ishii, Shin ^a,c  

^a NARA INSTITUTE OF SCIENCE AND TECHNOLOGY   (Japan)

^b OSAKA MEDICAL CENTER FOR CANCER AND CARDIOVASCULAR DISEASES   (Japan)

^c KYOTO UNIVERSITY   (Japan)

Author keywords

Cancer diagnosis.; Classifier design and evaluation; Error correcting output coding; Gene expression profiling; Medicine; Multiclass classification; Statistical

Indexed keywords

CANCER DIAGNOSIS.; CLASSIFIER DESIGN AND EVALUATION; ERROR CORRECTING OUTPUT CODING; GENE EXPRESSION PROFILING; MULTICLASS CLASSIFICATION; STATISTICAL;

BIOACTIVITY; BIOINFORMATICS; COMPUTER AIDED DIAGNOSIS; GENE EXPRESSION; LEARNING SYSTEMS; OPTIMIZATION;

CLASSIFIERS;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; CLASSIFICATION; COMPUTER SIMULATION; ESOPHAGUS TUMOR; GENE EXPRESSION PROFILING; GENETICS; HUMAN; LEUKEMIA; NEOPLASM; REPRODUCIBILITY; STATISTICAL MODEL; THYROID TUMOR;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; COMPUTER SIMULATION; ESOPHAGEAL NEOPLASMS; GENE EXPRESSION PROFILING; HUMANS; LEUKEMIA; MODELS, STATISTICAL; NEOPLASMS; REPRODUCIBILITY OF RESULTS; THYROID NEOPLASMS;

EID: 67549135049     PISSN: 15455963     EISSN: None     Source Type: Journal    
DOI: 10.1109/TCBB.2007.70239     Document Type: Article

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