Prediction of beta-turns at over 80% accuracy based on an ensemble of predicted secondary structures and multiple alignments

BMC Bioinformatics

Volumn 9, Issue , 2008, Pages

  Zheng, Ce ^a   Kurgan, Lukasz ^a  

^a UNIVERSITY OF ALBERTA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

EMPIRICAL EVALUATIONS; MULTIPLE ALIGNMENT; POSITION SPECIFIC SCORING MATRIX; PREDICTION METHODS; PREDICTION QUALITY; PROTEIN SEQUENCES; SECONDARY PROTEIN STRUCTURE; SECONDARY STRUCTURES;

AMINO ACIDS; IMAGE RETRIEVAL; INFORMATION ANALYSIS; MATHEMATICAL MODELS; SUPPORT VECTOR MACHINES;

FORECASTING;

ASPARAGINE; ASPARTIC ACID; GLYCINE; ISOLEUCINE; LEUCINE; METHIONINE; PROLINE; PROTEIN;

ACCESS TO INFORMATION; ACCURACY; AMINO ACID SEQUENCE; ARTICLE; COMPUTER PROGRAM; EMPIRICISM; FALSE POSITIVE RESULT; INTERMETHOD COMPARISON; PREDICTION; PROTEIN SECONDARY STRUCTURE; PROTEIN STRUCTURE; SEQUENCE ALIGNMENT; SEQUENCE ANALYSIS; SUPPORT VECTOR MACHINE; ALGORITHM; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; AUTOMATED PATTERN RECOGNITION; BIOLOGY; CHEMISTRY; METHODOLOGY; PROTEIN DATABASE; PROTEIN FOLDING;

ALGORITHMS; AMINO ACID SEQUENCE; ARTIFICIAL INTELLIGENCE; COMPUTATIONAL BIOLOGY; DATABASES, PROTEIN; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, AUTOMATED; PROTEIN FOLDING; PROTEIN STRUCTURE, SECONDARY; PROTEINS; SEQUENCE ALIGNMENT;

EID: 58149284018     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-9-430     Document Type: Article

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