Improve Glioblastoma Multiforme Prognosis Prediction by Using Feature Selection and Multiple Kernel Learning

IEEE/ACM Transactions on Computational Biology and Bioinformatics

Volumn 13, Issue 5, 2016, Pages 825-835

  Zhang, Ya ^a   Li, Ao ^a   Peng, Chen ^a   Wang, Minghui ^a  

^a UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA   (China)

Author keywords

GBM; Glioblastoma multiforme; mRMR; multiple kernel learning

Indexed keywords

DISEASES; FEATURE EXTRACTION; FORECASTING;

CLASSIFICATION METHODS; CLASSIFICATION RULES; FEATURE SELECTION METHODS; GLIOBLASTOMA MULTIFORME; INTEGRATED MODELING; MRMR; MULTIPLE KERNEL LEARNING; PROGNOSIS PREDICTION;

CLASSIFICATION (OF INFORMATION);

ALGORITHM; AUTOMATED PATTERN RECOGNITION; BRAIN NEOPLASMS; CLASSIFICATION; CLINICAL DECISION SUPPORT SYSTEM; COMPUTER ASSISTED DIAGNOSIS; EVALUATION STUDY; FEMALE; GLIOBLASTOMA; HUMAN; MACHINE LEARNING; MALE; MIDDLE AGED; MORTALITY; PREVALENCE; PROCEDURES; PROGNOSIS; REPRODUCIBILITY; RISK ASSESSMENT; SENSITIVITY AND SPECIFICITY; SURVIVAL ANALYSIS;

ALGORITHMS; BRAIN NEOPLASMS; DECISION SUPPORT SYSTEMS, CLINICAL; DIAGNOSIS, COMPUTER-ASSISTED; FEMALE; GLIOBLASTOMA; HUMANS; MACHINE LEARNING; MALE; MIDDLE AGED; PATTERN RECOGNITION, AUTOMATED; PREVALENCE; PROGNOSIS; REPRODUCIBILITY OF RESULTS; RISK ASSESSMENT; SENSITIVITY AND SPECIFICITY; SURVIVAL ANALYSIS;

EID: 84994501850     PISSN: 15455963     EISSN: None     Source Type: Journal    
DOI: 10.1109/TCBB.2016.2551745     Document Type: Article

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