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. 

G. Hobson, "Neural Network Applications at PSP," paper presented at NPRA Computer Conference, Seattle, Wash., 1990. 

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. 

From the figure it is easy to see that an infinite number of lines can be drawn that separate the depressed points from the not depressed points in the plane. This is a characteristic of regression and neural network applications rarely is there one solution but a whole family of solutions for a given problem. Nevertheless, a perception can be easily trained to classify patients based on the two hypothetical measures posed. One perception with its trained weights for this particular set of data is shown in Figure 3.2. 

Self-organizing maps are, like most neural network applications, limited by the quality of the data that is used to train them. If the training data is not representative of the whole set of data to which a network is expected to apply, then the clusters found in training may not be representative. This is especially critical with small sets of training data. 

Figure 6.1 Design issues of neural network applications for genome informatics.

Table 9.1 Neural network applications for nucleic acid sequence analysis.

Table 10.1 summarizes neural network applications for protein structure prediction. Protein secondary structure prediction is often used as the first step toward understanding and predicting tertiary structure because secondary structure elements constitute the building blocks of the folding units. An estimated 90% or so of the residues in most proteins are involved in three classes of secondary structures, the a-helices, p-strands or reverse turns. Related to the secondary structure prediction are also the prediction of solvent accessibility, transmembrane helices, and secondary structure content (10.2). Neural networks have also been applied to protein tertiary structure prediction, such as the prediction of the backbones or side-chain packing, and to structural class prediction (10.3). 

Protein secondary structure prediction is one of the earliest neural network applications in molecular biology, and has been extensively reviewed. Typified by the work of Qian and Sejnowski (1988) (Figure 10.1), early studies involved the use of perception or three-... 

Neural network applications for protein sequence analysis are summarized in Table 11.1. Like the DNA coding region recognition problem, signal peptide prediction (11.2) involves both search for content and search for signal tasks. An effective means for protein sequence analysis is reverse database searching to detect functional motifs or sites (11.3) and identify protein families (11.4). Most of the functional motifs are also... 

Integration of Statistical Methods into Neural Network Applications... 

How have neural networks been used in genome informatics applications In Part II, we have summarized them based on the types of applications for DNA sequence analysis, protein structure prediction and protein sequence analysis. Indeed, the development of neural network applications over the years has resulted in many successful and widely used systems. Current state-of-the-art systems include those for gene recognition, secondary structure prediction, protein classification, signal peptide recognition, and peptide design, to name just a few. 

Anzali, S., Bamickel, G, Krug, M., Sadowski, J., Wagener, M., Gasteiger, J. and Polanski, J. (1996). The Comparison of Geometric and Electronic Properties of Molecular Surfaces by Neural Networks Application to the Analysis of Corticosteroid Binding Globulin Activity of Steroids. J.Comput.Aid.Molec.Des., 10, 521-534. 

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. (1995) Artifidal neural networks applications. Part 1. Estimation of the total it-electron energy of henzenoid hydrocarbons. Rev. Roum. Chim., 40, 1093-1101. 

Bodri, L. and V. Cermak Prediction of extreme precipitation using a neural network application to summer flood occurrence in Moravia. Adv. Eng. Software 31 (2000) 211 - 221. 

---