Exploiting heterogeneous features to improve in silico prediction of peptide status - amyloidogenic or non-amyloidogenic

BMC Bioinformatics

Volumn 12, Issue SUPPL. 13, 2011, Pages

  Nair, Smitha S K ^a   Subba Reddy, N V ^b   Hareesha, K S ^a  

^a MANIPAL INSTITUTE OF TECHNOLOGY   (India)

^b MODY UNIVERSITY OF SCIENCE AND TECHNOLOGY   (India)

Author keywords

[No Author keywords available]

Indexed keywords

AMINO ACID INDICES; AMYLOID FIBRIL; AMYLOID-LIKE FIBRIL; AMYLOIDOGENIC PEPTIDES; ARTIFICIAL NEURAL NETWORK MODELS; ATOMIC COMPOSITIONS; AUTOCORRELATION FUNCTIONS; CLASSIFIER DESIGN; CORRELATION COEFFICIENT; FEATURE VECTORS; HETEROGENEOUS FEATURES; HYBRID GENETIC ALGORITHMS; IN-SILICO; INTERACTION EFFECT; MEMETIC ALGORITHMS; MOLECULAR TECHNIQUES; OPTIMAL NUMBER; OPTIMIZATION STRATEGY; PREDICTION QUALITY; PREDICTION TOOLS; PROTEIN FRAGMENTS; PROTEIN SEQUENCES; RECEIVER OPERATING CHARACTERISTICS; SENSITIVITY AND SPECIFICITY; UNDERLYING CAUSE;

AMINO ACIDS; GENETIC ALGORITHMS; GLYCOPROTEINS; IMAGE RETRIEVAL; NEURAL NETWORKS; OPTIMIZATION; PEPTIDES; SUPPORT VECTOR MACHINES;

FORECASTING;

LACUNA;

AMYLOID; PEPTIDE;

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; CHEMISTRY; METHODOLOGY; PROTEIN DATABASE; PROTEIN FOLDING; PROTEIN TERTIARY STRUCTURE; RECEIVER OPERATING CHARACTERISTIC; SENSITIVITY AND SPECIFICITY; SEQUENCE ANALYSIS; SUPPORT VECTOR MACHINE;

ALGORITHMS; AMYLOID; DATABASES, PROTEIN; NEURAL NETWORKS (COMPUTER); PEPTIDES; PROTEIN FOLDING; PROTEIN STRUCTURE, TERTIARY; ROC CURVE; SENSITIVITY AND SPECIFICITY; SEQUENCE ANALYSIS, PROTEIN; SUPPORT VECTOR MACHINES;

EID: 84864052876     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-12-S13-S21     Document Type: Article

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