Artificial neural networks methods

Finally, the artificial neural network methods try to imitate human intelligence with the power of statistic. 

Artificial Neural Networks Methods and Applications, edited by David S. Livingstone,... 

Cai, Y. D., Yu, H. Chou, K. C. (1998a). Artificial neural network method for predicting HIV protease cleavage sites in protein. J. Protein Chem 17,607-15. 

Sun, Z., Rao, X., Peng, L. Xu, D. (1997). Prediction of protein supersecondary structures based on the artificial neural network method. Protein Eng 10,763-9. 

An inexperienced user or sometimes even an avid practitioner of QSAR could be easily con-fiased by the multitude of methodologies and naming conventions used in QSAR studies. Two-dimensional (2D) and three-dimensional (3D) QSAR, variable selection and artificial neural network methods, comparative molecular field analysis (CoMFA), and binary QSAR present examples of various terms that may appear to describe totally independent approaches, which cannot be even compared to each other. In fact, any QSAR method can be generally defined as the application of mathematical and statistical methods to the problem of finding empirical relationships (QSARmod-els)of the form, D . D ), where... 

Cai, Y. Chen, C. (1995). Artificial neural network method for discriminating coding regions of eukaryotic genes. Comput Appl Biosci 11(5), 497-501. 

Rose, V. S., Macfie, H. J. H., and Croall, I. F. (1991) Kohonen topology-preserving mapping an unsupervised artificial neural network method for use in QSAR analysis. Pharmacochem. Libr. 16,213-216. 

A unique stopped-FIA system for simultaneous synchronous spectrofluorometric online dissolution monitoring of multicomponent solid preparations that makes use of a fiberoptic sensor was presented by Li et al. [3]. A new means of in vitro therapeutic drug monitoring was developed by hyphenating the fiberoptic sensor technique and a chemometric method. An artificial neural network method was applied to construct the mathematical model for the simultaneous analysis of the mixture of vitamins Bj, B2, and Bg by synchronous spectrofluorometry. The selection of the wavelength interval, the pFI of the carrier solution, and other experimental conditions were evaluated. The proposed method has been applied to the dissolution monitoring of vitamin B tablets with satisfactory results. The recovery was 97.8%-105%, and the relative standard deviation (RSD) was 1.1-7.5. 

Tatliera, M., Cigizoglub, H.K. and Erdem-Senatalara, A. (2005). Artificial neural network methods for the estimation of zeolite molar compositions that form from different reaction mixtures, Comput. Chem. Eng., 30, 137-146. 

Interaction with aquifer geochemistry in this indirect method, multivariable statistics or artificial neural network method is used to analyze and link geochemical data to bioremediation or microbial community data. 

The method that was developed builds on computed values of physicochemical effects and uses neural networks for classification. Therefore, for a deeper understanding of this form of reaction classification, later chapters should be consulted on topics such as methods for the calculation of physicochemical effects (Section 7.1) and artificial neural networks (Section 9.4). 

A challenging task in material science as well as in pharmaceutical research is to custom tailor a compound s properties. George S. Hammond stated that the most fundamental and lasting objective of synthesis is not production of new compounds, but production of properties (Norris Award Lecture, 1968). The molecular structure of an organic or inorganic compound determines its properties. Nevertheless, methods for the direct prediction of a compound s properties based on its molecular structure are usually not available (Figure 8-1). Therefore, the establishment of Quantitative Structure-Property Relationships (QSPRs) and Quantitative Structure-Activity Relationships (QSARs) uses an indirect approach in order to tackle this problem. In the first step, numerical descriptors encoding information about the molecular structure are calculated for a set of compounds. Secondly, statistical and artificial neural network models are used to predict the property or activity of interest based on these descriptors or a suitable subset. 

A structure descriptor is a mathematical representation of a molecule resulting from a procedure transforming the structural information encoded within a symbolic representation of a molecule. This mathematical representation has to be invariant to the molecule s size and number of atoms, to allow model building with statistical methods and artificial neural networks. 

Chirality codes are used to represent molecular chirality by a fixed number of de-.scriptors. Thc.se descriptors can then be correlated with molecular properties by way of statistical methods or artificial neural networks, for example. The importance of using descriptors that take different values for opposite enantiomers resides in the fact that observable properties are often different for opposite enantiomers. 

Kohonen networks, also known as self-organizing maps (SOMs), belong to the large group of methods called artificial neural networks. Artificial neural networks (ANNs) are techniques which process information in a way that is motivated by the functionality of biological nervous systems. For a more detailed description see Section 9.5. 

Problems involving routine calculations are solved much faster and more reliably by computers than by humans. Nevertheless, there are tasks in which humans perform better, such as those in which the procedure is not strictly determined and problems which are not strictly algorithmic. One of these tasks is the recognition of patterns such as feces. For several decades people have been trying to develop methods which enable computers to achieve better results in these fields. One approach, artificial neural networks, which model the functionality of the brain, is explained in this section. 

Woodruff and co-workers introduced the expert system PAIRS [67], a program that is able to analyze IR spectra in the same manner as a spectroscopist would. Chalmers and co-workers [68] used an approach for automated interpretation of Fourier Transform Raman spectra of complex polymers. Andreev and Argirov developed the expert system EXPIRS [69] for the interpretation of IR spectra. EXPIRS provides a hierarchical organization of the characteristic groups that are recognized by peak detection in discrete ames. Penchev et al. [70] recently introduced a computer system that performs searches in spectral libraries and systematic analysis of mixture spectra. It is able to classify IR spectra with the aid of linear discriminant analysis, artificial neural networks, and the method of fe-nearest neighbors. 

Concomitantly with the increase in hardware capabilities, better software techniques will have to be developed. It will pay us to continue to learn how nature tackles problems. Artificial neural networks are a far cry away from the capabilities of the human brain. There is a lot of room left from the information processing of the human brain in order to develop more powerful artificial neural networks. Nature has developed over millions of years efficient optimization methods for adapting to changes in the environment. The development of evolutionary and genetic algorithms will continue. 

Srinivasula S, Jain A (2006) A comparative analysis of training methods for artificial neural network rainfall-runoff models. Appl Soft Comput 6 295-306... 

Takayama K, Fujikawa M, Nagai T. Artificial neural networks as a novel method to optimize pharmaceutical formulations. Pharm Res 1999 16 1-6. 

Kandimalla KK, Kanikkannon N, Singh M. Optimization of a vehicle mixture for the transdermal delivery of melatonin using artificial neural networks and response surface method. / Controlled Release 1999 61 71-82. 

In recent years there has been much activity to devise methods for multivariate calibration that take non-linearities into account. Artificial neural networks (Chapter 44) are well suited for modelling non-linear behaviour and they have been applied with success in the field of multivariate calibration [47,48]. A drawback of neural net models is that interpretation and visualization of the model is difficult. Several non-linear variants of PCR and PLS regression have been proposed. Conceptually, the simplest approach towards introducing non-linearity in the regression model is to augment the set of predictor variables (jt, X2, ) with their respective squared terms (xf,. ..) and, optionally, their possible cross-product... 

Wu et al. [46] used the approach of an artificial neural network and applied it to drug release from osmotic pump tablets based on several coating parameters. Gabrielsson et al. [47] applied several different multivariate methods for both screening and optimization applied to the general topic of tablet formulation they included principal component analysis and... 

In addition, methods of artificial intelligence (artificial neural networks and genetic algorithms) are applied. 

Li-Xian Sim, Danzer K, Thiel G (1997) Classification of wine samples by means of artificial neural networks and discrimination analytical methods. Fresenius J Anal Chem 359 143... 

It is time to turn to our first AI method Chapter 2, Artificial Neural Networks. 

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 ... 

---