Predicting mutagenicity of aromatic amines by various machine learning approaches

Toxicological Sciences

Volumn 116, Issue 2, 2010, Pages 498-513

  Leong, Max K ^a   Lin, Sheng Wen ^a   Chen, Hong Bin ^a   Tsai, Fu Yuan ^b  

^a NATIONAL DONG HWA UNIVERSITY   (Taiwan)

^b CHANG GUNG UNIVERSITY   (Taiwan)

Author keywords

Aromatic amines; Genetic function algorithm; Hierarchical support vector regression; Machine learning; Mutagenicity; Radial basis function neural networks; Support vector machine

Indexed keywords

AROMATIC AMINE; AMINE; MUTAGENIC AGENT;

ACCURACY; ARTICLE; COMPUTER MODEL; GENETIC ALGORITHM; HIERARCHICAL SUPPORT VECTOR REGRESSION; MACHINE LEARNING; MUTAGENICITY; NONHUMAN; PERFORMANCE MEASUREMENT SYSTEM; PREDICTION; QUANTITATIVE STRUCTURE ACTIVITY RELATION; RADIAL BASED FUNCTION; SUPPORT VECTOR MACHINE; VALIDATION STUDY; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; MUTAGEN TESTING; STATISTICAL MODEL; TOXICITY;

AMINES; ARTIFICIAL INTELLIGENCE; MODELS, STATISTICAL; MUTAGENICITY TESTS; MUTAGENS; NEURAL NETWORKS (COMPUTER); QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP;

EID: 77955085075     PISSN: 10966080     EISSN: 10960929     Source Type: Journal    
DOI: 10.1093/toxsci/kfq159     Document Type: Article

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