Improving the accuracy of predicting disulfide connectivity by feature selection

Journal of Computational Chemistry

Volumn 31, Issue 7, 2010, Pages 1478-1485

  Zhu, Lin ^a   Yang, Jie ^a   Song, Jiang Ning ^b   Chou, Kuo Chen ^a,c   Shen, Hong Bin ^a  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

^b KYOTO UNIVERSITY   (Japan)

^c GORDON LIFE SCIENCE INSTITUTE   (United States)

Author keywords

Disulfide connectivity prediction; Feature selection; Fisher score; Protein structure prediction; Support vector machine

Indexed keywords

COMPUTATIONAL PREDICTIONS; CONNECTIVITY PATTERN; COVALENT CROSS-LINKS; CYSTEINE RESIDUES; DIMENSIONAL SPACES; DISULFIDE BONDS; EFFICIENT FEATURE SELECTIONS; FEATURE REDUNDANCY; FEATURE SELECTION; FISHER SCORE; HIGH-DIMENSIONAL; HIGH-DIMENSIONAL FEATURE SPACE; NONLOCAL; OVERFITTING; POLYPEPTIDE CHAIN; PREDICTION PERFORMANCE; PRIMARY SEQUENCES; PROTEIN STRUCTURE PREDICTION; REDUNDANT INFORMATIONS; STATISTICAL LEARNING; STRUCTURAL INFORMATION; STRUCTURAL STUDIES;

FORECASTING; PROTEINS; SUPPORT VECTOR MACHINES;

FEATURE EXTRACTION;

CYSTEINE; DISULFIDE; PROTEIN; PROTEIN BINDING;

ARTICLE; BIOLOGY; CHEMISTRY; METABOLISM; METHODOLOGY; PREDICTION AND FORECASTING; PROTEIN BINDING; PROTEIN CONFORMATION; PROTEIN FOLDING; PREDICTIVE VALUE; PROCEDURES;

COMPUTATIONAL BIOLOGY; CYSTEINE; DISULFIDES; PREDICTIVE VALUE OF TESTS; PROTEIN BINDING; PROTEIN CONFORMATION; PROTEIN FOLDING; PROTEINS;

EID: 77950878660     PISSN: 01928651     EISSN: 1096987X     Source Type: Journal    
DOI: 10.1002/jcc.21433     Document Type: Article

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