H-DROP: An SVM based helical domain linker predictor trained with features optimized by combining random forest and stepwise selection

Journal of Computer-Aided Molecular Design

Volumn 28, Issue 8, 2014, Pages 831-839

  Ebina, Teppei ^a,b   Suzuki, Ryosuke ^a   Tsuji, Ryotaro ^a   Kuroda, Yutaka ^a  

^a TOKYO UNIVERSITY OF AGRICULTURE AND TECHNOLOGY   (Japan)

^b RIKEN BRAIN SCIENCE INSTITUTE   (Japan)

Author keywords

Domain boundary prediction; Feature selection; Helical linker; Machine learning; Random forest; Stepwise selection; Structural domain; Support vector machine

Indexed keywords

DECISION TREES; FEATURE SELECTION; MOLECULAR BIOLOGY; SUPPORT VECTOR MACHINES;

DOMAIN BOUNDARY; DOMAIN BOUNDARY PREDICTION; DOMAIN LINKERS; FEATURES SELECTION; HELICAL LINKER; MACHINE-LEARNING; RANDOM FORESTS; STEPWISE SELECTION; STRUCTURAL DOMAINS; SUPPORT VECTORS MACHINE;

FORECASTING;

PROTEIN;

AMINO ACID COMPOSITION; ARTICLE; CONTROLLED STUDY; PREDICTION; PRIORITY JOURNAL; PROCESS OPTIMIZATION; PROTEIN DOMAIN; PROTEIN SECONDARY STRUCTURE; RANDOM FOREST; SUPPORT VECTOR MACHINE; SURFACE PROPERTY; BIOLOGY; CHEMICAL STRUCTURE; CHEMISTRY; COMPUTER PROGRAM; PROCEDURES; PROTEIN DATABASE; PROTEIN TERTIARY STRUCTURE; PROTEOMICS;

COMPUTATIONAL BIOLOGY; DATABASES, PROTEIN; MODELS, MOLECULAR; PROTEIN STRUCTURE, TERTIARY; PROTEINS; PROTEOMICS; SOFTWARE; SUPPORT VECTOR MACHINES;

EID: 84904761886     PISSN: 0920654X     EISSN: 15734951     Source Type: Journal    
DOI: 10.1007/s10822-014-9763-x     Document Type: Article

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