Feature selection in metabolomics can identify important metabolite features that play a significant role in discriminating between various conditions among samples. In this paper, we propose an efficient feature selection method for high-resolution nuclear magnetic resonance (NMR) spectra obtained from time-course experiments. Our proposed approach combines linear-mixed effects (LME) models with a multiple testing procedure based on a false discovery rate. The proposed LME approach is illustrated using NMR spectra with 574 metabolite features obtained for an experiment to examine metabolic changes in response to sulfur amino acid intake. The experimental results showed that classification models constructed with the features selected by the proposed approach resulted in lower rates of misclassification than those models with full features. Furthermore, we compared the LME approach with the two-sample t-test approach that oversimplifies the time-course factor.
Bibliographical noteFunding Information:
We are grateful to Dean P. Jones and Thomas R. Zeigler in the Emory University Medical School for their useful comments. This work was support in part by NSF Grant ECCS-0801802.
- False discovery rate
- Feature selection
- Linear-mixed effects models
- Multiple hypothesis testing
- Nuclear magnetic resonance
ASJC Scopus subject areas
- Computer Science Applications
- Artificial Intelligence