Probabilistic neural networks

Custer, L. L, Durham, S. K., Pearl, G. M. Probabilistic neural network multiple classifier system for predicting the genotoxicity of quinolone and quinoline derivatives. Chem. Res. Toxicol. 2005, 18, 428-440. 

Niwa, T. Using general regression and probabilistic neural networks to predict human intestinal absorption with topological descriptors derived from two-dimensional chemical stmctures. 

Niwa, T. (2004) Prediction of biological targets using probabilistic neural networks and atom-type descriptors. Journal of Medicinal Chemistry, 47 (10), 2645-2650. 

Hernandez-Caraballo et al. [91,92] evaluated several classical chemometric methods and ANNs as screening tools for cancer research. They measured the concentrations of Zn, Cu, Fe and Se in blood serum specimens by total reflection XRF spectrometry. The classical chemometric approaches used were PCA and logistic regression. On the other hand, two neural networks were employed for the same task, viz., back-propagation and probabilistic neural networks. 

Rezzi, S., Axelson, D. E., Heberger, K., Reniero, F., Mariani, C., and Guillou, C. (2005). Classification of olive oils using high throughput flow 1H NMR fingerprinting with principal component analysis, linear discriminant analysis and probabilistic neural networks. Anal. Chim. Acta 552,13-24. 

Three commonly used ANN methods for classification are the perceptron network, the probabilistic neural network, and the learning vector quantization (LVQ) networks. Details on these methods can be found in several references.57,58 Only an overview of them will be presented here. In all cases, one can use all available X-variables, a selected subset of X-variables, or a set of compressed variables (e.g. PCs from PCA) as inputs to the network. Like quantitative neural networks, the network parameters are estimated by applying a learning rule to a series of samples of known class, the details of which will not be discussed here. 

Environment Canada Fathead minnow acute toxicity ECOSAR, TOPKAT, probabilistic neural network, computational neural network, ASTER, OASIS Moore et al. (2003)... 

Specht, D. F. (1990). Probabilistic neural networks. Neural Networks 2,110-18. 

Some historically important artificial neural networks are Hopfield Networks, Per-ceptron Networks and Adaline Networks, while the most well-known are Backpropa-gation Artificial Neural Networks (BP-ANN), Kohonen Networks (K-ANN, or Self-Organizing Maps, SOM), Radial Basis Function Networks (RBFN), Probabilistic Neural Networks (PNN), Generalized Regression Neural Networks (GRNN), Learning Vector Quantization Networks (LVQ), and Adaptive Bidirectional Associative Memory (ABAM). 

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. 

Niculescu SP, Atkinson A, Hammond G, Lewis M. Using fragment chemistry data mining and probabilistic neural networks in screening chemicals for acute toxicity to the fathead minnow. SAR QSAR Environ Res 2004 15(4) 293-309. 

Probabilistic neural network (PNN) is similar to GRNN except that it is used for classification problems [54], It has been used for pharmacodynamics [55], pharmacokinetics [34,56] studies and has recently been applied for genotoxicity [43,50,57] and torsade de pointes prediction [58], PNN classifies compounds into their data class through the use of Bayes s optimal decision rule ... 

Mosier PD, Jurs PC. QSAR/QSPR studies using probabilistic neural networks and generalized regression neural networks. J Chem Inf Comput Sci 2002 42 1460-70. 

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. 

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, 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. 

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

Holmes, E. Nicholson, J.K. Tranter, G. Metabonomic Characterization of Genetic Variations in Toxicological and Metabolic Responses Using Probabilistic Neural Networks, Chem. Res. Toxicol. 14(2), 182-191 (2001). 

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