Antz, C. and Neidig, K.-P. and Kalbitzer, Hans Robert (1995) A general Bayesian method for an automated signal class recognition in 2D NMR spectra combined with a multivariate discriminant analysis. Journal of Biomolecular NMR 5 (3), pp. 287-296.
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A generally applicable method for the automated classification of 2D NMR peaks has been developed, based on a Bayesian approach coupled to a multivariate linear discriminant analysis of the data. The method can separate true NMR signals from noise signals, solvent stripes and artefact signals. The analysis relies on the assumption that the different signal classes have different distributions of specific properties such as line shapes, line widths and intensities. As to be expected, the correlation network of the distributions of the selected properties affects the choice of the discriminant function and the final selection of signal properties. The classification rule for the signal classes was deduced from Bayes's theorem. The method was successfully tested on a NOESY spectrum of HPr protein from Staphylococcus aureus. The calculated probabilities for the different signal class memberships are realistic and reliable, with a high efficiency of discrimination between peaks that are true NOE signals and those that are not.
|Institutions:||Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Dr. Hans Robert Kalbitzer|
|Keywords:||Multivariate discriminant analysis - 2D NMR spectroscopy - NOESY - Bayesian analysis - Peak recognition - NMR molecular structure|
|Subjects:||500 Science > 570 Life sciences|
|Created at the University of Regensburg:||Unknown|
|Deposited On:||08 Sep 2010 09:59|
|Last Modified:||08 Sep 2010 09:59|
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