Prediction of toxicity using a novel RBF neural network training methodology

Journal of Molecular Modeling

Volumn 12, Issue 3, 2006, Pages 297-305

  Melagraki, Georgia ^a   Afantitis, Antreas ^a   Makridima, Kalliopi ^a   Sarimveis, Haralambos ^a   Igglessi Markopoulou, Olga ^a  

^a NATIONAL TECHNICAL UNIVERSITY OF ATHENS   (Greece)

Author keywords

Neural network; QSTR; RBF architecture; Tetrahymena pyriformis; Toxicity

Indexed keywords

2,3 DICHLOROPHENOL; 2,4,6 TRICHLOROPHENOL; GUAIACOL; PENTACHLOROPHENOL; PHENOL DERIVATIVE; PHLOROGLUCINOL;

ACCURACY; ARTICLE; ARTIFICIAL NEURAL NETWORK; CHEMICAL STRUCTURE; CONTROLLED STUDY; METHODOLOGY; MULTIPLE LINEAR REGRESSION ANALYSIS; NONHUMAN; PHYSICAL CHEMISTRY; PREDICTION; PRIORITY JOURNAL; QUANTITATIVE ANALYSIS; STATISTICAL SIGNIFICANCE; STRUCTURE ANALYSIS; TETRAHYMENA PYRIFORMIS; TOXICITY TESTING; VALIDATION PROCESS;

ANIMALS; NEURAL NETWORKS (COMPUTER); PHENOLS; TETRAHYMENA PYRIFORMIS;

TETRAHYMENA PYRIFORMIS;

EID: 33644781895     PISSN: 16102940     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00894-005-0032-8     Document Type: Article

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