Analysis of metabolomic data using support vector machines.

By Sankar Mahadevan, Sirish L. Shah, T. Marrie, C. Slupsky. Analytical Chemistry, 2008

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Analytical Methods in Untargeted Metabolomics: State of the Art in 2015 (2015, neutral): “Support vector machines (SVMs) are another class of supervised analysis methods to build classifiers based on metabolomic data (Mahadevan et al., 2008; Kim et al., 2010; Luts et al., 2010).”
Multivariate Analysis in Metabolomics. (2012, methodology): “Support-vector machine (SVM) methods [118] have been applied to human urine metabolomics NMR [119], NIR and UV datasets [120] with enhanced predictive power over PLS-DA.”
Capillary electrophoresis mass spectrometry-based saliva metabolomics identified oral, breast and pancreatic cancer-specific profiles (2009, methodology): “In this study, although we used stepwise feature selection and an MLR model to identify classifiers, other feature selection and classification methods are also applicable, such as regression tree models (Li et al. 2004, 2006) and concurrent use of ANN with SVM (Ayers et al. 2004).”
A tutorial review: Metabolomics and partial least squares-discriminant analysis--a marriage of convenience or a shotgun wedding. (2015, methodology): “[120] two multivariate analyses, PLS-DA and SVM, were compared in terms of feature selection and classification.”