Nontargeted metabolite fingerprinting is increasingly applied to biomedical classification. The choice of classification algorithm may have a considerable impact on outcome. In this study, employing nested cross-validation for assessing predictive performance, six binary classification algorithms in combination with different strategies for data-driven feature selection were systematically compared on five data sets of urine, serum, plasma, and milk one-dimensional fingerprints obtained by proton nuclear magnetic resonance (NMR) spectroscopy. Support Vector Machines and Random Forests combined with t-score-based feature filtering performed well on most data sets, whereas the performance of the other tested methods varied between data sets. © 2012 American Chemical Society.
CITATION STYLE
Hochrein, J., Klein, M. S., Zacharias, H. U., Li, J., Wijffels, G., Schirra, H. J., … Gronwald, W. (2012). Performance evaluation of algorithms for the classification of metabolic 1H NMR fingerprints. Journal of Proteome Research, 11(12), 6242–6251. https://doi.org/10.1021/pr3009034
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