Accurate prediction of solvent accessibility using neural networks-based regression

Proteins: Structure, Function and Genetics

Volumn 56, Issue 4, 2004, Pages 753-767

  Adamczak, Rafał ^a   Porollo, Aleksey ^a   Meller, Jarosław ^a,b  

^a CINCINNATI CHILDREN S HOSPITAL RESEARCH FOUNDATION   (United States)

^b NICOLAUS COPERNICUS UNIVERSITY   (Poland)

Author keywords

Approximation; Classification; Neural networks; Protein structure prediction; Regression; Relative solvent accessibility; SABLE; Solvent exposure

Indexed keywords

AMINO ACID; PROTEIN; SOLVENT;

ACCURACY; ALGORITHM; ANALYTIC METHOD; ARTICLE; ARTIFICIAL NEURAL NETWORK; CLASSIFICATION; COMPUTER ANALYSIS; CORRELATION COEFFICIENT; DISSOLUTION; ERROR; INTERMETHOD COMPARISON; MATHEMATICAL PARAMETERS; MOLECULAR EVOLUTION; NONLINEAR REGRESSION ANALYSIS; PFAM DATABASE; PREDICTION; PRIORITY JOURNAL; PROTEIN DATA BANK; PROTEIN DATABASE; PROTEIN FAMILY; PROTEIN STRUCTURE; REGRESSION APPROXIMATION; RELATIVE SOLVENT ACCESSIBILITY; SEQUENCE HOMOLOGY; THERMOMETER ENCODING; VALIDATION PROCESS;

ARTIFICIAL INTELLIGENCE; COMPUTATIONAL BIOLOGY; DATABASES, PROTEIN; EVOLUTION, MOLECULAR; NEURAL NETWORKS (COMPUTER); PREDICTIVE VALUE OF TESTS; PROTEIN STRUCTURE, QUATERNARY; PROTEINS; REGRESSION ANALYSIS; SEQUENCE HOMOLOGY, AMINO ACID; SOLUBILITY; SOLVENTS;

EID: 4043052866     PISSN: 08873585     EISSN: None     Source Type: Journal    
DOI: 10.1002/prot.20176     Document Type: Article

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