Evolutionary optimization of kernel weights improves protein complex comembership prediction

IEEE/ACM Transactions on Computational Biology and Bioinformatics

Volumn 6, Issue 3, 2009, Pages 427-437

  Hulsman, Marc ^a   Reinders, Marcel J T ^a   De Ridder, Dick ^a  

^a University of Technology ^*  (Netherlands)

Author keywords

Biology and genetics; Evolutionary computing and genetic algorithms; Keywords classifier design and evaluation

Indexed keywords

AREA UNDER THE ROC CURVE; BEST STRATEGY; BIOLOGY AND GENETICS; CLASSIFIER PERFORMANCE; DATA SOURCE; EVOLUTIONARY COMPUTING AND GENETIC ALGORITHMS; EVOLUTIONARY OPTIMIZATIONS; GENE SEQUENCES; HIGH-THROUGHPUT DATA; INDIVIDUAL PERFORMANCE; KERNEL BASED CLASSIFIERS; KERNEL WEIGHT; KEYWORDS CLASSIFIER DESIGN AND EVALUATION; MRNA EXPRESSION; PERFORMANCE IMPROVEMENTS; PROTEIN COMPLEXES;

BIOLOGY; CHROMOSOMES; CLASSIFIERS; COMPUTATION THEORY; GENETIC ALGORITHMS; LEARNING SYSTEMS; OPTIMIZATION;

SCALES (WEIGHING INSTRUMENTS);

MULTIPROTEIN COMPLEX;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; BIOLOGICAL MODEL; CHEMISTRY; MOLECULAR EVOLUTION; NONLINEAR SYSTEM; REPRODUCIBILITY; ROC CURVE; STATISTICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; EVOLUTION, MOLECULAR; LINEAR MODELS; MODELS, GENETIC; MULTIPROTEIN COMPLEXES; NONLINEAR DYNAMICS; REPRODUCIBILITY OF RESULTS; ROC CURVE;

EID: 68649126782     PISSN: 15455963     EISSN: None     Source Type: Journal    
DOI: 10.1109/TCBB.2008.137     Document Type: Article

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