A classification framework applied to cancer gene expression profiles

Journal of Healthcare Engineering

Volumn 4, Issue 2, 2013, Pages 255-283

  Hijazi, Hussein ^a   Chan, Christina ^a,b  

^a Michigan State University   (United States)

^b Michigan State University   (United States)

Author keywords

Cancer; Classification; Feature selection; Gene expression; Machine learning; Supervised learning

Indexed keywords

CLASSIFICATION (OF INFORMATION); DECISION TREES; FEATURE EXTRACTION; GENE EXPRESSION; LEARNING SYSTEMS; PROTEINS; SUPERVISED LEARNING; SUPPORT VECTOR MACHINES;

CANCER; CANCER GENE EXPRESSION; CLASSIFICATION FRAMEWORK; CLASSIFICATION METHODS; CLASSIFICATION PERFORMANCE; FEATURE SELECTION METHODS; MACHINE LEARNING METHODS; PROTEIN-PROTEIN INTERACTIONS;

DISEASES;

ARTICLE; CANCER CLASSIFICATION; DECISION TREE; DISEASE CLASSIFICATION; GENE EXPRESSION; GENETIC ALGORITHM; HUMAN; K NEAREST NEIGHBOR; MACHINE LEARNING; PROTEIN PROTEIN INTERACTION; SUPPORT VECTOR MACHINE; ALGORITHM; COMPUTER ASSISTED DIAGNOSIS; GENE EXPRESSION PROFILING; METABOLISM; METHODOLOGY; NEOPLASM; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

TUMOR MARKER; TUMOR PROTEIN;

ALGORITHMS; DIAGNOSIS, COMPUTER-ASSISTED; GENE EXPRESSION PROFILING; HUMANS; NEOPLASM PROTEINS; NEOPLASMS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; TUMOR MARKERS, BIOLOGICAL;

MLCS; MLOWN;

EID: 84881234696     PISSN: 20402295     EISSN: 20402309     Source Type: Journal    
DOI: 10.1260/2040-2295.4.2.255     Document Type: Article

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