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The partial least squares-discriminant analysis (PLS-DA) is the most widely used statistical tool to perform classification and biomarker screening in metabolomics. However, the PLS-DA tends to overfit the data, and the selection of biomarkers is often unstable because of the disturbance of uninformative variables in principal components. In this paper, we propose an algorithm for performing stable biomarker screening and for seeking the optimal generalization performance, in which the biomarker identification is based on sparse regularization variable selection in combination with subsampling (SRS), and the classification is subsequently performed by a linear support vector machine (SVM) classifier in the selected-variable space to obtain the maximum classification accuracy. Two metabolomics datasets measured by gas chromatography-mass spectrometry are employed to evaluate the performance of the proposed SRS-SVM algorithm, and the comparison with existing related algorithms is given. The result shows that the SRS-SVM algorithm outperforms the PLS-DA and is competitive with other related algorithms in terms of prediction classification accuracy measured by both internal and external validation. Furthermore, the selection of candidate biomarkers is quite stable by the SRS-SVM algorithm, and it can be an alternative and competitive method for the analysis of metabolomics data. The R code for implementing the SRS-SVM algorithm is available in the Electronic supplementary material . [ABSTRACT FROM AUTHOR]

This paper is the generalization of weight-fused elastic net (Fu and Xu, 2012), which performs group variable selection by combining weight-fused LASSO(wfLasso) and elastic net (Zou and Hastie, 2005) penalties. In this study, the elastic net penalty is replaced by adaptive elastic net penalty (AdaEnet) (Zou and Zhang, 2009), and a new group variable selection algorithm with oracle property (Fan and Li, 2001; Zou, 2006) is obtained. [ABSTRACT FROM PUBLISHER]

In this article, we consider the problem of variable selection and estimation with the strongly correlated multi-collinear data by using grouping variable selection techniques. A new grouping variable selection method, called weight-fused elastic net(WFEN), is proposed to deal with the high dimensional collinear data. The proposed model, combined two different grouping effect mechanisms induced by the elastic net and weight-fused LASSO, respectively, can be easily unified in the frame of LASSO and computed efficiently. The performance with the simulation and real data sets shows that our method is competitive with other related methods, especially when the data present high multi-collinearity. [ABSTRACT FROM PUBLISHER]