Prediction of N-linked glycan branching patterns using artificial neural networks

Mathematical Biosciences

Volumn 211, Issue 1, 2008, Pages 89-104

  Senger, Ryan S ^a   Karim, M Nazmul ^b  

^a UNIVERSITY OF DELAWARE   (United States)

^b Department of Electrical and Computer Engineering   (United States)

Author keywords

Artificial intelligence; CHO cells; Complex type; Endoplasmic reticulum; Glycan branching patterns; Glycosylation; Golgi apparatus; High mannose; N Linked; Neural network

Indexed keywords

CELLS; GROWTH KINETICS; NITROGEN COMPOUNDS; PATTERN RECOGNITION; PROTEINS; TISSUE;

CHO CELLS; ENDOPLASMIC RETICULUM; GLYCAN BRANCHING PATTERNS; GOLGI APPARATUS; HIGH MANNOSE;

NEURAL NETWORKS;

GLYCAN; RECOMBINANT PROTEIN; TISSUE PLASMINOGEN ACTIVATOR;

ARTIFICIAL NEURAL NETWORK; MUTATION; NITROGEN; PREDICTION; PROTEIN;

AMINO ACID SEQUENCE; ARTICLE; ARTIFICIAL NEURAL NETWORK; GLYCOSYLATION; NONHUMAN; PREDICTION; PROTEIN ANALYSIS; PROTEIN EXPRESSION; PROTEIN LOCALIZATION; PROTEIN MODIFICATION; PROTEIN STRUCTURE;

ALGORITHMS; ANIMALS; CHO CELLS; COMPUTER SIMULATION; CRICETINAE; CRICETULUS; GLYCOPROTEINS; GLYCOSYLATION; MANNOSE; NEURAL NETWORKS (COMPUTER); OLIGOSACCHARIDES, BRANCHED-CHAIN; PROTEIN CONFORMATION; PROTEIN PROCESSING, POST-TRANSLATIONAL; SOLVENTS; TISSUE PLASMINOGEN ACTIVATOR;

EID: 37349076888     PISSN: 00255564     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.mbs.2007.10.005     Document Type: Article

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