Multiple classifier integration for the prediction of protein structural classes

Journal of Computational Chemistry

Volumn 30, Issue 14, 2009, Pages 2248-2254

  Chen, Lei ^a   Lu, Lin ^b   Feng, Kairui ^c   Li, Wenjin ^d   Song, Jie ^e   Zheng, Lulu ^e   Yuan, Youlang ^f   Zeng, Zhenbin ^a   Feng, Kaiyan ^g   Lu, Wencong ^f   Cai, Yudong ^d,f  

^a EAST CHINA NORMAL UNIVERSITY   (China)

^b SHANGHAI JIAO TONG UNIVERSITY   (China)

^c CERTARA UK LIMITED   (United Kingdom)

^d MAX PLANCK INSTITUTE   (Germany)

^e HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^f SHANGHAI UNIVERSITY   (China)

^g UNIVERSITY OF MANCHESTER   (United Kingdom)

Author keywords

Amino acid compositions; Minimum redundancy maximum relevance; Multiple classifier integration; Protein structural class; Weka

Indexed keywords

ALGORITHM SELECTION; AMINO ACID COMPOSITIONS; ARTIFICIAL NEURAL NETWORK; BASIC TRAINING; BIOLOGICAL DATA; CLASSIFICATION ALGORITHM; CLASSIFICATION RESULTS; DATA SETS; FEATURE SELECTION; INDIVIDUAL CLASSIFIERS; INTEGRATION STRATEGY; MACHINE LEARNING ALGORITHMS; MACHINE-LEARNING; MAJORITY VOTE; MAJORITY VOTING; MINIMUM REDUNDANCY MAXIMUM RELEVANCE; MULTIPLE CLASSIFIER COMBINATION; MULTIPLE CLASSIFIER INTEGRATION; MULTIPLE CLASSIFIERS; PREDICTION AND ANALYSIS; SAN FRANCISCO; SINGLE MACHINES; SOFTWARE TOOL; STRUCTURAL CLASS; SUPERVISED CLASSIFIERS; TEST SETS; WEB SERVERS; WEKA;

AMINATION; AMINES; AMINO ACIDS; CHARACTER RECOGNITION; CLASSIFIERS; DATA MINING; INTEGRATION; MACHINE TOOLS; NEURAL NETWORKS; ORGANIC ACIDS; QUALITY ASSURANCE; REDUNDANCY; ROBOT LEARNING; STATISTICAL TESTS; VOTING MACHINES;

LEARNING ALGORITHMS;

PROTEIN;

ALGORITHM; ARTICLE; CHEMISTRY; COMPUTER SIMULATION; FACTUAL DATABASE; PROTEIN CONFORMATION;

ALGORITHMS; COMPUTER SIMULATION; DATABASES, FACTUAL; PROTEIN CONFORMATION; PROTEINS;

EID: 70349466390     PISSN: 01928651     EISSN: 1096987X     Source Type: Journal    
DOI: 10.1002/jcc.21230     Document Type: Article

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