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Assessment of statistical and neural networks methods in NMR spectral classification and metabolite selection by P. J. G. Lisboa; S. P. J. Kirby; A. Vellido; Y. Y. B. Lee; W. El-Deredy is a Medicine article available to read on EtoBox.

What is Assessment of statistical and neural networks methods in NMR spectral classification and metabolite selection about?

Magnetic resonance spectroscopy opens a window into the biochemistry of living tissue. However, spectra acquired from different tissue types in vivo or in vitro and from body fluids contain a large number of peaks from a range of metabolites, whose relative intensities vary substantially and in complicated ways even between successive samples from the same category. The realization of the full clinical potential of NMR spectroscopy relies, in part, on our ability to interpret and quantify the role of individual metabolites in characterizing specific tissue and tissue conditions. This paper addresses the problem of tissue classification by analysing NMR spectra using statistical and neural network methods. It assesses the performance of classification models from a range of statistical methods and compares them with the performance of artificial neural network models. The paper also assesses the consistency of the models in selecting, directly from the spectra, the subsets of metabolites most relevant for differentiating between tissue types. The analysis techniques are examined using in vitro spectra from eight classes of normal tissue and tumours obtained from rats. We show that,

Who reads Assessment of statistical and neural networks methods in NMR spectral classification and metabolite selection?

Author

P. J. G. Lisboa; S. P. J. Kirby; A. Vellido; Y. Y. B. Lee; W. El-Deredy

Publisher

John Wiley and Sons; Wiley (John Wiley & Sons); John Wiley & Sons Inc.; Wiley (ISSN 0952-3480)

Published

1998

Language

EN

Field

Medicine (Health Sciences)