An Integrative Computational Framework Based on a Two-Step Random Forest Algorithm Improves Prediction of Zinc-Binding Sites in Proteins

PLoS ONE

Volumn 7, Issue 11, 2012, Pages

  Zheng, Cheng ^a   Wang, Mingjun ^a   Takemoto, Kazuhiro ^b   Akutsu, Tatsuya ^c   Zhang, Ziding ^d   Song, Jiangning ^a,c,e  

^a Tianjin Airport Economic Area   (China)

^b KYUSHU INSTITUTE OF TECHNOLOGY   (Japan)

^c KYOTO UNIVERSITY   (Japan)

^d CHINA AGRICULTURAL UNIVERSITY   (China)

^e MONASH UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ASPARTIC ACID; CYSTEINE; GLUTAMIC ACID; HISTIDINE;

ACCURACY; ALGORITHM; AMINO ACID SEQUENCE; ARTICLE; BINDING SITE; CONTROLLED STUDY; INTERMETHOD COMPARISON; PREDICTION; PROTEIN STRUCTURE; RANDOM FOREST; STATISTICAL ANALYSIS; VALIDATION STUDY; ZINC BINDING SITE;

ALGORITHMS; AMINO ACIDS; APOPROTEINS; BINDING SITES; COMPUTATIONAL BIOLOGY; INTERNET; METALLOPROTEINS; MODELS, BIOLOGICAL; PROTEIN BINDING; REPRODUCIBILITY OF RESULTS; ROC CURVE; ZINC;

EID: 84869115085     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0049716     Document Type: Article

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