Support vector machines for predicting rRNA-, RNA-, and DNA-binding proteins from amino acid sequence

Biochimica et Biophysica Acta - Proteins and Proteomics

Volumn 1648, Issue 1-2, 2003, Pages 127-133

  Cai, Yu Dong ^a,c   Lin, Shuo Liang ^b  

^a INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

^b WYETH RESEARCH   (United States)

^c UNIVERSITY OF MANCHESTER   (United Kingdom)

Author keywords

Classification; Feature vector; Function; Functional genomic; Machine learning; Prediction; Pseudo amino acid composition; Support vector machine, SVM

Indexed keywords

AMINO ACID DERIVATIVE; DNA BINDING PROTEIN; PROTEIN DERIVATIVE; RIBOSOME RNA; RNA; RNA BINDING PROTEIN;

ACCURACY; AMINO ACID COMPOSITION; AMINO ACID SEQUENCE; ARTICLE; CORRELATION ANALYSIS; CROSS LINKING; EXPERIMENTAL TEST; HYDROPHOBICITY; MACHINE; MOLECULAR DYNAMICS; MOLECULAR INTERACTION; PREDICTION; PRIORITY JOURNAL; PROTEIN ANALYSIS; PROTEIN FUNCTION; SUPPORT VECTOR MACHINE; SURFACE PROPERTY; VALIDATION PROCESS; BIOLOGY; CHEMISTRY; GENETICS; PROTEIN TERTIARY STRUCTURE; SEQUENCE ANALYSIS;

COMPUTATIONAL BIOLOGY; DNA-BINDING PROTEINS; PROTEIN STRUCTURE, TERTIARY; RNA-BINDING PROTEINS; SEQUENCE ANALYSIS, PROTEIN;

EID: 0038644483     PISSN: 15709639     EISSN: None     Source Type: Journal    
DOI: 10.1016/S1570-9639(03)00112-2     Document Type: Article

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