Linear-mixed effects models for feature selection in high-dimensional NMR spectra
作者： Yajun Mei , Seoung Bum Kim , Kwok-Leung Tsui
DOI: 10.1016/J.ESWA.2008.06.032
关键词:
摘要: Feature selection in metabolomics can identify important metabolite features that play a significant role discriminating between various conditions among samples. In this paper, we propose an efficient feature method for high-resolution nuclear magnetic resonance (NMR) spectra obtained from time-course experiments. Our proposed approach combines linear-mixed effects (LME) models with multiple testing procedure based on false discovery rate. The LME is illustrated using NMR 574 experiment to examine metabolic changes response sulfur amino acid intake. experimental results showed classification constructed the selected by resulted lower rates of misclassification than those full features. Furthermore, compared two-sample t-test oversimplifies factor.
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