AcconPred: Predicting solvent accessibility and contact number simultaneously by a multitask learning framework under the conditional neural fields model

BioMed Research International

Volumn 2015, Issue , 2015, Pages

  Ma, Jianzhu ^a   Wang, Sheng ^a,b  

^a TOYOTA TECHNOLOGICAL INSTITUTE AT CHICAGO   (United States)

^b UNIVERSITY OF CHICAGO   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AMINO ACID; SOLVENT; PROTEIN;

AMINO ACID SEQUENCE; ARTICLE; CONDITIONAL NEURAL FIELD MODEL; LEARNING; MACHINE LEARNING; MATHEMATICAL PHENOMENA; MODEL; PREDICTION; PROTEIN ANALYSIS; PROTEIN CONFORMATION; PROTEIN FOLDING; PROTEIN FUNCTION; PROTEIN STRUCTURE; SURFACE AREA; ALGORITHM; ARTIFICIAL NEURAL NETWORK; BINDING SITE; CHEMISTRY; METABOLISM; MOLECULAR MODEL; PROTEIN DATABASE; PROTEIN SECONDARY STRUCTURE;

ALGORITHMS; AMINO ACID SEQUENCE; BINDING SITES; DATABASES, PROTEIN; MODELS, MOLECULAR; NEURAL NETWORKS (COMPUTER); PROTEIN CONFORMATION; PROTEIN FOLDING; PROTEIN STRUCTURE, SECONDARY; PROTEINS; SOLVENTS;

EID: 84938356212     PISSN: 23146133     EISSN: 23146141     Source Type: Journal    
DOI: 10.1155/2015/678764     Document Type: Article

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