Prediction of contact maps by GIOHMMs and recurrent neural networks using lateral propagation from all four cardinal corners

Bioinformatics

Volumn 18, Issue SUPPL. 1, 2002, Pages

  Pollastri, G ^a   Baldi, P ^b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Graphical models; Protein contacts, contact map; Protein structure prediction; Recurrent neural networks

Indexed keywords

ARTIFICIAL NEURAL NETWORK; BAYES THEOREM; CONFERENCE PAPER; INFORMATION PROCESSING; PATTERN RECOGNITION; PREDICTION; PRIORITY JOURNAL; PROTEIN LOCALIZATION; PROTEIN STRUCTURE; PROTEOMICS; ALGORITHM; AMINO ACID SEQUENCE; ARTICLE; ARTIFICIAL INTELLIGENCE; BINDING SITE; CHEMICAL STRUCTURE; CHEMISTRY; COMPARATIVE STUDY; EVALUATION; FEEDBACK SYSTEM; METHODOLOGY; MOLECULAR GENETICS; PROBABILITY; PROTEIN BINDING; PROTEIN CONFORMATION; PROTEIN FOLDING; SEQUENCE ALIGNMENT; SEQUENCE ANALYSIS; STATISTICAL MODEL; VALIDATION STUDY;

PROTEIN;

ALGORITHMS; AMINO ACID SEQUENCE; ARTIFICIAL INTELLIGENCE; BINDING SITES; FEEDBACK; MARKOV CHAINS; MODELS, MOLECULAR; MODELS, STATISTICAL; MOLECULAR SEQUENCE DATA; NEURAL NETWORKS (COMPUTER); PROTEIN BINDING; PROTEIN CONFORMATION; PROTEIN FOLDING; PROTEINS; SEQUENCE ALIGNMENT; SEQUENCE ANALYSIS, PROTEIN;

EID: 0041719954     PISSN: 13674803     EISSN: 13674811     Source Type: Journal    
DOI: 10.1093/bioinformatics/18.suppl_1.S62     Document Type: Conference Paper

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