Predictive Bayesian neural network models of MHC class II peptide binding

Journal of Molecular Graphics and Modelling

Volumn 23, Issue 6, 2005, Pages 481-489

  Burden, Frank R ^b,c   Winkler, David A ^a,c  

^a CSIRO   (Australia)

^b SciMetrics   (Australia)

^c MONASH UNIVERSITY   (Australia)

Author keywords

Bayesian neural networks; Major histocompatibility complex; Peptide binding; Quantitative structure activity relationships; T cell epitope

Indexed keywords

AMINO ACIDS; EMBEDDED SYSTEMS; FUZZY SETS; GRAPH THEORY; MATHEMATICAL MODELS; NEURAL NETWORKS; STATISTICAL METHODS;

BAYESIAN NEURAL NETWORK MODELS; NANOMERS; PEPTIDE BINDING; RECEIVER OPERATING CHARACTERISTICS (ROC ) GRAPHS;

POLYPEPTIDES;

AMINO ACID; HLA DR ANTIGEN; MAJOR HISTOCOMPATIBILITY ANTIGEN CLASS 2;

AMINO ACID SEQUENCE; ARTICLE; ARTIFICIAL NEURAL NETWORK; BAYES THEOREM; MOLECULAR MODEL; PRIORITY JOURNAL; PROTEIN BINDING; RECEIVER OPERATING CHARACTERISTIC; STATISTICAL ANALYSIS; VALIDATION PROCESS;

AMINO ACID SEQUENCE; BAYES THEOREM; HISTOCOMPATIBILITY ANTIGENS CLASS II; HUMANS; MOLECULAR SEQUENCE DATA; NEURAL NETWORKS (COMPUTER); OLIGOPEPTIDES; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP;

EID: 20444386168     PISSN: 10933263     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jmgm.2005.03.001     Document Type: Article

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