Toxicity, neural network modeling

Kaiser KLE, Niculescu SP, Schultz TW. Probabilistic neural network modeling for the toxicity of chemicals to Tetrahymena pyriformis with molecular fragment descriptors. SAR QSAR Environ Res 2002 13 57-67. 

Devillers J. Prediction of toxicity of organophosphorus insecticides against the midge, Chironomus riparius, via a QSAR neural network model integrating environmental variables. Toxicol Meth 2000 10 69-79. 

Niculescu, S. P., Kaiser, K. L. E., and Schuurmann, G. (1998) Influence of data preprocessing and kernel selection on probabilistic neural network modeling of the acute toxicity of chemicals to the fathead minnow and Vibrio fischeri bacteria. Water Qual. Res. J. Can. 33, 153-165. 

Correlation ranking and stepwise regression procedures in principal components artificial neural networks modeling with application to predict toxic activity and human serum albumin binding affinity... 

Examples of statistical analysis for QSAR developing using different techniques is given in Table 2 that shows the prediction of the Daphnia magna toxicity for allelochemicals (BOA, DIMBOA and MBOA) using QSARs models obtained with different statistical techniques (PLS, MLR, and Neural Networks). 

Burden, F. R., and Winkler, D. A. (2000) A quantitative structure-activity relationships model for the acute toxicity of substituted benzenes to Tetrahymena pyriformis using Bayesian-regularized neural networks. Chem. Res. Toxicol. 13,436-440. 

Another important feature of mathematical modeling techniques is the nature of the response data that they are capable of handling. Some methods are designed to work with data that are measured on a nominal or ordinal scale this means the results are divided into two or more classes that may bear some relation to one another. Male and female, dead and alive, and aromatic and nonaromatic, are all classifications (dichotomous in this case) based on a nominal scale. Toxic, slightly toxic, and non-toxic are classifications based on an ordinal scale since they can be written as toxic > slightly toxic > non-toxic. The rest of this section is divided into three parts methods that deal with classified responses, methods that handle continuous data, and artificial neural networks that can be used for both. 

Kaiser, K.L.E. and Niculescu, S.P. (1999). Using Probabilistic Neural Networks to Model the Toxicity of Chemicals to the Fathead Minnow (Pimephales promelas) A Study Based on 865 Compounds. Chemosphere, 38,3237-3245. 

Piotrowski PL, Sumter BG, Mailing HV, et al. A toxicity evaluation and predictive system based on neural networks and wavelets. / Chem Inf Model. 2007 47 676-685. 

Kaiser KLE, Niculescu SP. Using probabilistic neural networks to model the toxicity of chemicals to the fathead minnow (Pimephales promelas) A study based on 865 compounds. Chemosphere 1999 38 3237 15. 

With the advent of powerful computers and easy access to them, and the introduction of expert systems, artificial intelligence, and neural networks in QSAR, radically different models designed from noncongeneric large sets of chemicals have been proposed. No attempts are made to design a model that is easily interpretable in terms of MOA. The main objective of the present models is to provide powerful simulators with a wide domain of application for predicting the toxicity of any kind of molecule. 

Niculescu SP, Kaiser KLE Schultz TW. Modeling the toxicity of chemicals to Tetrahymena pyriformis using molecular fragment descriptors and probabilistic neural networks. Arch Environ Contam Toxicol 2000 39 289-98. 

Kaiser, KLE, Niculescu SP. Modeling acute toxicity of chemicals to Daphnia magna A probabilistic neural network approach. Environ Toxicol Chem 2001 20 420-31. 

Mazzatorta P, Benfenati E, Neagu CD, Gini G. Tuning neural and fuzzy-neural networks for toxicity modeling. J Chem Inf Comput Sci 2003 43 513-8. 

Devillers J. A QSAR model for predicting the acute toxicity of pesticides to gammarids. In Leardi R, editor, Nature-inspired methods in chemometrics Genetic algorithms and artificial neural networks. Amsterdam Elsevier, 2003. p. 323-39. 

Mazzatorta, P., VraHco, M., Jezierska, A. and Benfenati, E. (2003b) Modeling toxicity by using supervised Kohonen neural networks./. Chem. Inf Comput. Sci., 43, 485—492. 

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