Improving the prediction accuracy of residue solvent accessibility and real-value backbone torsion angles of proteins by guided-learning through a two-layer neural network

Proteins: Structure, Function and Bioinformatics

Volumn 74, Issue 4, 2009, Pages 847-856

  Faraggi, Eshel ^a,b   Xue, Bin ^a,b   Zhou, Yaoqi ^a,b  

^a Indiana University   (United States)

^b INDIANA UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

Artificial neural networks; Dihedral angles; Protein structure prediction; Solvent accessible surface area

Indexed keywords

PROTEIN; SOLVENT;

ACCURACY; AMINO ACID SEQUENCE; ARTICLE; ARTIFICIAL NEURAL NETWORK; CONTROLLED STUDY; LEARNING; MECHANICAL TORSION; PRIORITY JOURNAL; PROTEIN DATABASE; PROTEIN STRUCTURE; BINDING SITE; CHEMICAL STRUCTURE; CHEMISTRY; COMPUTER SIMULATION; PROTEIN CONFORMATION;

BINDING SITES; COMPUTER SIMULATION; DATABASES, PROTEIN; MODELS, MOLECULAR; NEURAL NETWORKS (COMPUTER); PROTEIN CONFORMATION; PROTEINS; SOLVENTS;

EID: 61449123967     PISSN: 08873585     EISSN: 10970134     Source Type: Journal    
DOI: 10.1002/prot.22193     Document Type: Article

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