Genetic algorithm-based feature selection in high-resolution NMR spectra

Hyun Woo Cho, Seoung Bum Kim, Myong K. Jeong, Youngja Park, Thomas R. Ziegler, Dean P. Jones

Research output: Contribution to journalArticle

32 Citations (Scopus)

Abstract

High-resolution nuclear magnetic resonance (NMR) spectroscopy has provided a new means for detection and recognition of metabolic changes in biological systems in response to pathophysiological stimuli and to the intake of toxins or nutrition. To identify meaningful patterns from NMR spectra, various statistical pattern recognition methods have been applied to reduce their complexity and uncover implicit metabolic patterns. In this paper, we present a genetic algorithm (GA)-based feature selection method to determine major metabolite features to play a significant role in discrimination of samples among different conditions in high-resolution NMR spectra. In addition, an orthogonal signal filter was employed as a preprocessor of NMR spectra in order to remove any unwanted variation of the data that is unrelated to the discrimination of different conditions. The results of k-nearest neighbors and the partial least squares discriminant analysis of the experimental NMR spectra from human plasma showed the potential advantage of the features obtained from GA-based feature selection combined with an orthogonal signal filter.

Original languageEnglish
Pages (from-to)967-975
Number of pages9
JournalExpert Systems with Applications
Volume35
Issue number3
DOIs
Publication statusPublished - 2008 Oct 1
Externally publishedYes

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Keywords

  • Discrimination
  • Feature selection
  • Genetic algorithm (GA)
  • Metabolomics
  • Nuclear magnetic resonance (NMR)
  • Orthogonal signal correction filter

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications

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