Neural networks Generalized regression

General regression neural network methodology is potentially more useful for predicting the QSPR relationships as compared to multiple linear regressions. 

Yap CW, Chen YZ (2005) Quantitative Structure-Pharmacokinetic Relationships for drug distribution properties by using general regression neural network. J Pharm Sci 94 153-168. 

M. Catasus, W. Branagh and E. D. Salin, Improved calibration for inductively coupled plasma-atomic emission spectrometry using generalized regression neural networks, Appl. Spectrosc., 49(6), 1995, 798-807. 

Zheng, G., Huang, W.H., Lu, X.H. (2003) Prediction of n-octanol/water partition coefficients for polychlorinated dibenzo-p-dioxins using a general regression neural network. AnalBioanal. Chem. 376, 680-685. 

One other network that has been used with supervised learning is the radial basis function (RBF) network.f Radial functions are relatively simple in form, and by definition must increase (or decrease) monotonically with the distance from a certain reference point. Gaussian functions are one example of radial functions. In a RBF network, the inputs are fed to a layer of RBFs, which in turn are weighted to produce an output from the network. If the RBFs are allowed to move or to change size, or if there is more than one hidden layer, then the RBF network is non-linear. An RBF network is shown schematically for the case of n inputs and m basis functions in Fig. 3. The generalized regression neural network, a special case of the RBF network, has been used infrequently especially in understanding in vitro-in vivo correlations. 

Specht, D.F. A generalized regression neural network. IEEE Trans. Neural Networks 1991, 2, 568-576. 

Ibric, S. Jovanivic, M. Djuric, Z. Parjcic J. Solomun, L. The application of a generalized regression neural network in the modelling and optimization of aspirin extended release tablets with Eudragit RSPO as matrix substance. J. Controlled Release 2002, 82, 213-222. 

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). 

Panaye A, Fan BT, Doucet JP, Yao XJ, Zhang RS, et al. Quantitative structure-toxicity relationships (QSTRs) A comparative study of various nonlinear methods. General regression neural network, radial basis function neural network and support vector machine in predicting toxicity of nitro- and cyano- aromatics to Tetrahymena pyriformis. SAR QSAR Environ Res 2006 17 75-91. 

General regression neural network (GRNN) was introduced by Donald Specht in 1991 [33], and it has been successfully used in pharmacokinetic studies, including human intestinal absorption [34], blood-brain barrier prediction [35], human serum albumin binding [35], milk-plasma ratio [35], and drug clearance [36], Recently it has been applied for the prediction of Tetrahymenapyriformis toxicity [37],... 

Specht DF. A general regression neural network. IEEE Trans on Neural Netw 1991 2 568-76. 

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. 

Initially, networks were trained from data obtained from the experimental design conditions given in Figure 7.3. These were radial basis function (RBF) networks, multilayer perception (MLP) networks, probabilistic neural networks (PNNs), and generalized regression neural networks (GRNNs), as well... 

D. F. Specht, IEEE Trans. Neural Networks, 2, 568 (1991). A General Regression Neural Network. 

Y. Xiang, J. Tian, Z. Zhang, and Y. Dai, Diagnosis of Endometrial Cancer Based on Near Infrared Spectroscopy and General Regression Neural Network, in Proceedings—2010 6th International Conference on Natural Computation, ICNC 2010, 2010, vol. 3, p. 1228. 

Ren and Gao [27] developed a technique which combines data fusion (DF) and multiscale wavelet transformations with generalized regression neural network (GRNN) and applied it for analyzing overlapping spectra. The main role of the hybrid method, DF-GRNN, is to enhance... 

Gobburu, J. V. S., Shelver, W. H., and Chen, E. P. (1996) Initial assessment of generalized regression neural networks (GRNN) in QSAR. Abstr. Pap. Am. Chem. Soc. 212th, MEDI-004. 

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