Improved protein secondary structure prediction using support vector machine with a new encoding scheme and an advanced tertiary classifier

IEEE Transactions on Nanobioscience

Volumn 3, Issue 4, 2004, Pages 265-271

  Hu, Hae Jin ^a   Pan, Yi ^a   Harrison, Robert ^a   Tai, Phang C ^a  

^a GEORGIA STATE UNIVERSITY   (United States)

Author keywords

Binary classifier; BLOSUM62; Encoding scheme; Orthorgonal matrix; Position Specific Scoring Matrix (PSSM); Support vector machine (SVM); Tertiary classifier

Indexed keywords

AMINO ACIDS; AROMATIC COMPOUNDS; BINARY CODES; DECODING; ENCODING (SYMBOLS); HYDROPHOBICITY; LEARNING ALGORITHMS; LEARNING SYSTEMS; NEURAL NETWORKS; OPTIMIZATION; PATTERN RECOGNITION; SET THEORY; VECTORS;

BINARY CLASSIFIERS; BLOSUM62; ENCODING SCHEMES; ORTHOGONAL MATRICES; POSITION SPECIFIC SCORING MATRIX (PSSM);

PROTEINS;

PROTEIN;

ALGORITHM; AMINO ACID SEQUENCE; ARTICLE; ARTIFICIAL INTELLIGENCE; CHEMICAL MODEL; CHEMICAL STRUCTURE; CHEMISTRY; CLASSIFICATION; COMPARATIVE STUDY; EVALUATION; METHODOLOGY; MOLECULAR GENETICS; PROTEIN CONFORMATION; PROTEIN SECONDARY STRUCTURE; PROTEIN TERTIARY STRUCTURE; SEQUENCE ALIGNMENT; SEQUENCE ANALYSIS; VALIDATION STUDY;

ALGORITHMS; AMINO ACID SEQUENCE; ARTIFICIAL INTELLIGENCE; MODELS, CHEMICAL; MODELS, MOLECULAR; MOLECULAR SEQUENCE DATA; PROTEIN CONFORMATION; PROTEIN STRUCTURE, SECONDARY; PROTEIN STRUCTURE, TERTIARY; PROTEINS; SEQUENCE ALIGNMENT; SEQUENCE ANALYSIS, PROTEIN;

EID: 10644246923     PISSN: 15361241     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNB.2004.837906     Document Type: Article

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