Somatic mutation detection using ensemble of flexible neural tree model

Neurocomputing

Volumn 179, Issue , 2016, Pages 161-168

  Yang, Bin ^a   Chen, Yuehui ^b  

^a ZAOZHUANG UNIVERSITY   (China)

^b UNIVERSITY OF JINAN   (China)

Author keywords

Ensemble learning; Flexible neural tree model; Next generation sequencing technology; Particle swarm optimization; Somatic mutations

Indexed keywords

FEATURE EXTRACTION; FORESTRY; PARTICLE SWARM OPTIMIZATION (PSO); RADIAL BASIS FUNCTION NETWORKS; TUMORS;

CLASSIFICATION ACCURACY; CLASSIFICATION ENSEMBLES; ENSEMBLE LEARNING; FLEXIBLE NEURAL TREE MODEL; NEXT-GENERATION SEQUENCING; NONLINEAR COMBINATION; RADIAL BASIS FUNCTION NEURAL NETWORKS; SOMATIC MUTATION;

TREES (MATHEMATICS);

ARTICLE; ARTIFICIAL NEURAL NETWORK; BAYESIAN LEARNING; CONTROLLED STUDY; FALSE POSITIVE RESULT; FLEXIBLE NEURAL TREE MODEL; GERMLINE MUTATION; LEARNING ALGORITHM; LOGISTIC REGRESSION ANALYSIS; MEASUREMENT ACCURACY; MEASUREMENT PRECISION; MUTATIONAL ANALYSIS; NEXT GENERATION SEQUENCING; PRIORITY JOURNAL; RADIAL BASED FUNCTION; RANDOM FOREST; SENSITIVITY AND SPECIFICITY; SOMATIC MUTATION; SUPPORT VECTOR MACHINE; VALIDATION PROCESS;

EID: 84955686357     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2015.12.001     Document Type: Article

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