Neural-network analysis

Nettles DL, Haider MA, Chilkoti A et al (2010) Neural network analysis identifies scaffold properties necessary for in vitro chondrogenesis in elastin-like polypeptide biopolymer scaffolds. Tissue Eng A 16 11-20... 

Turkoglu J, Ozarslan R, Sakr A. Artificial neural network analysis of a direct compression tabletting study. Eur J Pharm Biopharm 1995 41 315-22. 

C.W. McCarrick, D.T. Ohmer, L.A. Gilliland, P.A. Edwards and H.T. Mayfield, Fuel identification by neural network analysis of the responses of vapour-sensitive sensor arrays. Anal. Chem., 68 (1996) 4264-4269. 

Serretti, A. Smeraldi, E. (2004). Neural network analysis in pharmacogenetics of mood disorders. BMC Med. Genet., 5(1), 27. 

By design, ANNs are inherently flexible (can map nonlinear relationships). They produce models well suited for classification of diverse bacteria. Examples of pattern analysis using ANNs for biochemical analysis by PyMS can be traced back to the early 1990s.4fM7 In order to better demonstrate the power of neural network analysis for pathogen ID, a brief background of artificial neural network principles is provided. In particular, backpropagation artificial neural network (backprop ANN) principles are discussed, since that is the most commonly used type of ANN. 

Freeman, R. Goodacre, R. Sisson, P. R. Magee, J. G. Ward, A. C. Lightfoot, N. F. Rapid identification of species within the Mycobacterium tuberculosis complex by artificial neural network analysis of pyrolysis mass spectra. J. Med. Microbiol. 1994, 40,170-173. 

Chun, J. Atalan, E. Ward, A. C. Goodfellow, M. Artificial neural network analysis of pyrolysis mass spectrometric data in the identification of Streptomyces strains. FEMS Microbiol. Lett. 1993,107,321-325. 

Another way for BOD estimation is the use of sensor arrays [37]. An electronic nose incorporating a non-specific sensor array of 12 conducting polymers was evaluated for its ability to monitor wastewater samples. A statistical approach (canonical correlation analysis) showed a linear relationship between the sensor responses and BOD over 5 months for some subsets of samples, leading to the prediction of BOD values from electronic nose analysis using neural network analysis. 

Zheng E, Zheng G, Deaciuc AG Zhan CG Dwoskin LP, Crooks PA. (2007) Computational neural network analysis of the affinity of lobeline and tetra-benazine analogs for the vesicnlar monoamine transporter-2. Bioorg Med Chem 15 2975-2992. 

Ziegler, C., Harsch, A., and Gopel, W. (2000). Natural neural networks for quantitative sensing of neurochemicals An artificial neural network analysis. Sens. Actuators B Chem. 65,160-162. 

Gutes, A., Cespedes, F., Alegret, S., and del Valle, M. (2005). Determination of phenolic compounds by a polyphenol oxidase amperometric biosensor and artificial neural network analysis. Biosens. Bioelectron. 20(8), 1668-1673. 

Z. Roger, Selection of the quasi-optimal inputs in chemometric modelling by artificial neural networks analysis, Anal. Chim. Acta, 490(1-2), 2003, 31-40. 

Edin, B.B. and Trulsson, M.(1992) Neural network analysis of the information content in population responses from human periodontal receptors. Science of Neural Networks SPIE 1710 257-266... 

Control based on neural network. Similar to fuzzy logic modeling, neural network analysis uses a series of previous data to execute simulations of the process, with a high degree of success, without however using formal mathematical models (Chen and Rollins, 2000). To this goal, it is necessary to define inputs, outputs, and how many layers of neurons will be used, which depends on the number of variables and the available data. 

Several SARs and QSARs have been derived from data on antineoplastic activity as well as for MDR-reversing activity. The Hansch approach, Free-Wilson, and neural network analysis have been applied. The importance of lipophilicity, molar refractiv-ity (MR), and charge for the description of activity is common to all derived relations. 

Barratt, M.D., Quantitative structure-activity relationships (QS ARs) for skin corrosivity of organic acids, bases and phenols principal components and neural network analysis of extended datasets, Toxicol, in vitro, 10, 85-94, 1996b. 

Nykamp D. Q., Trandrina D. A population density approach that facilitates large-scale modeling of neural networks analysis and an application to orientation tuning. J Comput Neurosci, 2000, 8(1), 19-50. 

Wilcox, G. L Poliac, M. O. Sc Liebman, M. N. (1991). Neural network analysis of protein tertiary structure. Tetrahedron Comp Meth 3,191-211. 

Bakken and Jurs classify three types of models. A type 1 model uses multiple regression analysis to find a linear equation involving a descriptor set. This is the type we have discussed so far—and focus on—in this chapter. A type 2 model uses neural network analysis to develop a linear/nonlinear... 

Chemometric methods such as analysis of correlation coefficients, cluster analysis or neural network analysis are used, for example, in the classification of fragments of glass on the basis of their elemental composition or refractive index. Such methods allow the test material to be classified into the appropriate group of products on the basis of the measured parameter. 

N.E. Rapid Identification of Species within the Mycobacterium Tuberculosis Complex by Artificial Neural Network Analysis of Pyrolysis Mass Spectra, J. Med. Microbiol. 40(3), 170-173 (1994). 

Boydston-White, S., Romeo, M.) Chernenko, T Regina, A., Miljkovic, M. and Diem, M. (2006) Cell-cycle-dependent variations in FTIR micro-spedra of single proliferating HeLa cells principal component and artificial neural network analysis. Biochim. Biophys. Acta, 1758 (7), 908-14. 

MA Kramer and JA Leonard. Diagnosis using backpropagation neural networks - Analysis and criticism. Comput. Chem. Engg., 14 1323-1338, 1990. 

Kothari R, Cualing H, Balachander T. Neural network analysis of flow cytometry immunophenotype data. IEEE Trans Biomed Eng 1996 43(8) 803-810. 

As suggested in reference [25], the traditional sigmoidal function can be replaced with the Morlet wavelet basis function Fqwt in neural network analysis (Fig. 4(b)). When a spectral data, X, is applied to this WNN system, a response or an output value Ydwt >s obtained as follows ... 

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