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Abstract: Large amounts of data from high-throughput metabolomics experiments become commonly more and more complex, which brings an enormous amount of challenges to existing statistical modeling. Thus there is a need to develop statistically efficient approach for mining the underlying metabolite information contained by metabolomics data under investigation. In the work, we developed a novel kernel Fisher discriminant analysis (KFDA) algorithm by constructing an informative kernel based on decision tree ensemble. The constructed kernel can effectively encode the similarities of metabolomics samples between informative metabolites/biomarkers in specific parts of the measurement space. Simultaneously, informative metabolites or potential biomarkers can be successfully discovered by variable importance ranking in the process of building kernel. Moreover, KFDA can also deal with nonlinear relationship in the metabolomics data by such a kernel to some extent. Finally, two real metabolomics datasets together with a simulated data were used to demonstrate the performance of the proposed approach through the comparison of different approaches. [Copyright &y& Elsevier]

Learning from class-imbalanced data has gained increasing attention in recent years due to the massive growth of skewed data across many scientific fields such as metabolomics. Some researches show that it is not the imbalance itself which hinders the classification performance, but class overlapping do play an important role in the performance degradation when associated to class-imbalance. So alleviating the overlapping of the imbalanced data might be an effective way to improve the performance in class-imbalance learning. In this study, we propose two feature selection algorithms that aim to minimize the overlap degree between the majority and the minority, which is based on a simple assumption that decreasing overlap degree of a data set makes it more separable. The proposed MOSNS and MOSS methods are built via sparse regularization techniques. Simulation results indicate that our algorithms is effective in recognizing key features and control false discoveries for class-imbalance learning. Four class-imbalanced metabolomics data sets are also employed to test the performance of our algorithm, and a comparison with accuracy (ACC)-based and ROC-based selection procedures is performed. The result shows that our algorithms are highly competitive and can be an alternative feature selection strategy in class-imbalance learning. ● Two feature selection algorithms are proposed by minimizing the overlap of a data set. ● Sparse feature selection strategy are used in class-imbalance learning. ● The classification performance of metabolomics data can be improved by sparse feature selection and re-balance preprocessing. [ABSTRACT FROM AUTHOR]