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Author Notes:

Corresponding author: sbkim@uta.edu

Subjects:

Research Funding:

This study was supported by grants from the National Institutes of Health: R03 DK066008, R03 ES012929, R01 ES011195, R01 DK55850, and the Emory General Clinical Research Center grant M01 RR00039.

Keywords:

  • Nuclear Magnetic Resonance
  • NMR
  • feature selection
  • metabolomics
  • multivariate statistical analysis
  • Orthogonal Signal Correction
  • OSC

Discovery of metabolite features for the modelling and analysis of high-resolution NMR spectra

Tools:

Journal Title:

International Journal of Data Mining and Bioinformatics

Volume:

Volume 2, Number 2

Publisher:

, Pages 176-192

Type of Work:

Article | Post-print: After Peer Review

Abstract:

High-resolution Nuclear Magnetic Resonance (NMR) spectroscopy in combination with multivariate statistical methods has been widely used to investigate metabolic fluctuations in biological systems. This study presents three feature selection methods for identifying the metabolite features that contribute to the distinction of spectral samples among varying nutritional conditions in human plasma. Loading vectors of Principal Component Analysis (PCA), the optimal discriminant direction of Fisher discriminant analysis, and index values of the Variable Importance in Projection (VIP) in a Partial Least Square Discriminant Analysis (PLS-DA) were used to calculate the importance of individual metabolite feature in spectra. In addition, an Orthogonal Signal Correction (OSC) filter was used to eliminate unnecessary variations in NMR spectra and its effectiveness was demonstrated through PCA and kernel PCA. For the evaluation of presented feature selection methods, we compared the ability of classification based on the metabolite features selected by each method. The results have shown that the best classification was achieved using VIP values from an OSC-PLS-DA model.

Copyright information:

© 2008 Inderscience Enterprises Ltd.

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