An Overview of Metabonomics Techniques and Applications

Lindon, John C.

doi:10.1002/9780470571224.pse338

Cited by 1 publication

(1 citation statement)

References 54 publications

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“…A variety of techniques are available for studying metabolomics; of these, high-resolution NMR spectroscopy has an advantage because it requires minimal sample preparation, and is noninvasive or minimally invasive and can be adapted to a high-throughput format (Lindon, 2004). Fig.…”

Section: Introductionmentioning

confidence: 99%

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

Cho

Kim

Jeong

et al. 2008

Expert Systems with Applications

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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.

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Section: Introductionmentioning

confidence: 99%