Linear-mixed effects models for feature selection in high-dimensional NMR spectra

Yajun Mei, Seoung Bum Kim, Kwok Leung Tsui

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)4703-4708
Number of pages6
JournalExpert Systems With Applications
Issue number3 PART 1
Publication statusPublished - 2009 Apr
Externally publishedYes

Bibliographical note

Funding 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

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence


Dive into the research topics of 'Linear-mixed effects models for feature selection in high-dimensional NMR spectra'. Together they form a unique fingerprint.

Cite this