LigandRFs: Random forest ensemble to identify ligand-binding residues from sequence information alone

BMC Bioinformatics

Volumn 15, Issue , 2014, Pages

  Chen, Peng ^a,b   Huang, Jianhua Z ^c   Gao, Xin ^a  

^a KING ABDULLAH UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Saudi Arabia)

^b ANHUI UNIVERSITY   (China)

^c TEXAS A AND M UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); DECISION TREES; FORECASTING; LIGANDS; PROTEINS;

COMPUTATIONAL PREDICTIONS; LIGAND-BINDING RESIDUES; LIGAND-BINDING SITES; PROTEIN-LIGAND BINDING SITES; SEQUENCE INFORMATIONS; STATE OF THE ART; STATE-OF-THE-ART METHODS; STRUCTURAL INFORMATION;

BINDING SITES;

AMINO ACID; LIGAND; PROTEIN;

ARTIFICIAL INTELLIGENCE; BINDING SITE; CHEMISTRY; EVALUATION STUDY; PROCEDURES; PROTEIN CONFORMATION; SEQUENCE ANALYSIS;

AMINO ACIDS; ARTIFICIAL INTELLIGENCE; BINDING SITES; LIGANDS; PROTEIN CONFORMATION; PROTEINS; SEQUENCE ANALYSIS, PROTEIN;

EID: 84934951264     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: None     Document Type: Article

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