Artificial neural network applications

Goodacre, R. Edmonds, A. N. Kell, D. B. Quantitative analysis of the pyrolysis-mass spectra of complex mixtures using artificial neural networks Application to amino acids in glycogen. J. Anal. Appl. Pyrolysis 1993, 26, 93-114. 

Goodacre, R. Karim, A. Kaderbhai, M. A. Kell, D. B. Rapid and quantitative analysis of recombinant protein expression using pyrolysis mass spectrometry and artificial neural networks Application to mammalian cytochrome b5 in Escherichia coli. J. Biotechnol. 1994,34,185-193. 

Manallack, D.T. and Livingstone, D.J., Artificial neural networks application and chance effects for QSAR data analysis, Med. Chem. Res., 2, 181-190, 1992. 

Ivanciuc, O., Artificial neural networks applications. Part 7. Estimation of bioconcentration factors in fish using solvatochromic parameters, Revue Roumaine de Chimie, 43, 347-354, 1988. 

Ivanciuc, O. (1996). Artificial Neural Networks Applications. 2. Using Theoretical Descriptors of Molecular Structure in Quantitative Structure-Activity Relationships Analysis of the Inhibition of Dihydrofolate Reductase. Rev.Roum.Chim., 41,645-652. 

Hemmateenejad B, Akhond M, Miri R, Shamsipur M. Genetic algorithm applied to the selection of factors in principal component-artificial neural networks application to QSAR study of calcium channel antagonist activity of 1,4-dihydropyri-dines (nifedipine analogs). J Chem Inf Comput Sci 2003 43 1328-34. 

Ivanciuc, O. (1997) Artificial neural networks applications. Part 3. A quantitative structure-activity relationship for the actinidin hydrolysis of substituted-phenyl hippurates. Rev. Roum. Chim., 42, 325-332. 

Marengo E., Robotti E., Bobba M., Liparota M.C., Artificial neural networks applications in the field of separation science optimisation. Curr. Anal. Chem., 2, 181-194 (2006). 

D. Cirovic, Trends Anal. Chem., 16,148 (1997). Feed-Forward Artificial Neural Networks Applications to Spectroscopy. 

In recent decades, much attention has been paid to the application of artificial neural networks as a tool for spectral interpretation (see, e.g.. Refs. [104, 105]). The ANN approach app]ied to vibrational spectra allows the determination of adequate functional groups that can exist in the sample, as well as the complete interpretation of spectra. Elyashberg [106] reported an overall prediction accuracy using ANN of about 80 % that was achieved for general-purpose approaches. Klawun and Wilkins managed to increase this value to about 95% [107]. 

The results presented here imply that a similar approadi can be used for comparing two different hbraries, for determining the degree of overlap between the compounds in these two Hbraries. Examples of the application of artificial neural networks or GA in drug design are given in [57, 58, 84, 85]. 

Chen et al. [24] provide a good review of Al techniques used for modeling environmental systems. Pongracz et al. [25] presents the application of a fuzzy-rule based modeling technique to predict regional drought. Artificial neural networks model have been applied for mountainous water-resources management in Cyprus [26] and to forecast raw-water quality parameters for the North Saskatchewan River [27]. 

Maddalena DJ. Applications of artificial neural networks to quantitative structure-activity relationships. Expert Opin Ther Patents 1996 6 239-51. 

Bourquin J, Schmidli H, van Hoogevest P, Leuenberger H. Basic concepts of artificial neural networks (ANN) modelling in the application to pharmaceutical development. Pharm Dev Technol 1997 2 95-109. 

Sheng H, Wang P, Tu J.-S, Yuan L, Pin Q-N. Applications of artificial neural networks to the design of sustained release matrix tablets. Chinese J Pharmaceut 1998 29 352-4. 

Girosi, F., and Anzellotti, G., Rales of convergence for radial basis functions and neural networks. Artificial Neural Networks with Applications in Speech and Vision, (R. J. Matttmone, ed.), p. 97. Chapman Hall, London, 1993. 

Aqueous solubility is selected to demonstrate the E-state application in QSPR studies. Huuskonen et al. modeled the aqueous solubihty of 734 diverse organic compounds with multiple linear regression (MLR) and artificial neural network (ANN) approaches [27]. The set of structural descriptors comprised 31 E-state atomic indices, and three indicator variables for pyridine, ahphatic hydrocarbons and aromatic hydrocarbons, respectively. The dataset of734 chemicals was divided into a training set ( =675), a vahdation set (n=38) and a test set (n=21). A comparison of the MLR results (training, r =0.94, s=0.58 vahdation r =0.84, s=0.67 test, r =0.80, s=0.87) and the ANN results (training, r =0.96, s=0.51 vahdation r =0.85, s=0.62 tesL r =0.84, s=0.75) indicates a smah improvement for the neural network model with five hidden neurons. These QSPR models may be used for a fast and rehable computahon of the aqueous solubihty for diverse orgarhc compounds. 

H.M. Wei, L.S. Wang, B.G. Zhang, C.J. Liu and J.X. Feng, An application of artificial neural networks. Simultaneous determination of the concentration of sulfur dioxide and relative humidity with a single coated piezoelectric crystal. Anal. Chem., 69 (1997) 699-702. 

R. Goodacre, J. Pygall and D.B. Kell, Plant seed classification using pyrolysis mass spectrometry with unsupervised learning the application of auto-associative and Kohonen artificial neural networks. Chemom. Intell. Lab. Syst., 33 (1996) 69-83. 

Goodacre, R. Neal, M. I Kell, D. B. Quantitative analysis of multivariate data using artificial neural networks A tutorial review and applications to the deconvolution of pyrolysis mass spectra. Zbl. Bakt. 1996,284, 516-539. 

Another form of artificial intelligence is realized in artificial neural networks (ANN). The principle of ANNs has been presented in Sect. 6.5. Apart from calibration, data analysis and interpretation is one of the most important fields of application of ANNs in analytical chemistry (Tusar et al. [1991] Zupan and Gasteiger [1993]) where two branches claim particular interest ... 

The brain s remarkable ability to learn through a process of pattern recognition suggests that, if we wish to develop a software tool to detect patterns in scientific or, indeed, any other kind of data, the structure of the brain could be a productive starting point. This view led to the development of artificial neural networks (ANNs). The several methods that are gathered under the ANN umbrella constitute some of the most widely used applications of Artificial Intelligence in science. Typical areas in which ANNs are of value include ... 

Artificial neural networks are as common outside science as they are within it, particularly in financial applications, such as credit scoring and share selection. They have even been used in such eccentric (but, perhaps, financially rewarding) activities as trying to predict the box office success of motion pictures.1... 

There has been a notable change in the way that the GA has been used as it has become more popular in science. Some of the earliest applications of the GA in chemistry (for example, the work of Hugh Cartwright and Robert Long, and Andrew Tuson and Hugh Cartwright on chemical flowshops) used the GA as the sole optimization tool many more recent applications combine the GA with a second technique, such as an artificial neural network. 

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