Predicting carcinogenicity of diverse chemicals using probabilistic neural network modeling approaches

Toxicology and Applied Pharmacology

Volumn 272, Issue 2, 2013, Pages 465-475

  Singh, Kunwar P ^a,b   Gupta, Shikha ^a,b   Rai, Premanjali ^a,b  

^a ACADEMY OF SCIENTIFIC AND INNOVATIVE RESEARCH ACSIR   (India)

^b CSIR INDIAN INSTITUTE OF TOXICOLOGY RESEARCH   (India)

Author keywords

Carcinogenicity; Diversity; Generalized regression neural network; Interspecies model; Molecular descriptors; Probabilistic neural network

Indexed keywords

CARCINOGEN; PESTICIDE;

ANIMAL CELL; ANIMAL EXPERIMENT; ARTICLE; ARTIFICIAL NEURAL NETWORK; CARCINOGENICITY; CHEMICAL STRUCTURE; CONTROLLED STUDY; HAMSTER; HUMAN; HUMAN CELL; HYDROLYSIS; MOUSE; NERVE CELL; NONHUMAN; OXIDATION; RAT; TAUTOMERIZATION;

CARCINOGENICITY; DIVERSITY; GENERALIZED REGRESSION NEURAL NETWORK; INTERSPECIES MODEL; MOLECULAR DESCRIPTORS; PROBABILISTIC NEURAL NETWORK;

ANIMALS; CARCINOGENS; CRICETINAE; DATABASES, FACTUAL; MICE; MODELS, STATISTICAL; NEURAL NETWORKS (COMPUTER); PREDICTIVE VALUE OF TESTS; RATS; REGRESSION ANALYSIS; SPECIES SPECIFICITY;

EID: 84884148379     PISSN: 0041008X     EISSN: 10960333     Source Type: Journal    
DOI: 10.1016/j.taap.2013.06.029     Document Type: Article

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