Review and Research on Feature Selection Methods from NMR Data in Biological Fluids. Presentation of an Original Ensemble Method Applied to Atherosclerosis Field

ISSN: 1875-5453 (Online)
ISSN: 1389-2002 (Print)

Volume 18, 12 Issues, 2017

Download PDF Flyer

Current Drug Metabolism

This journal supports open access

Aims & ScopeAbstracted/Indexed in

Ranking and Category:
  • 125th of 289 in Biochemistry & Molecular Biology
  • 82nd of 254 in Pharmacology & Pharmacy

Submit Abstracts Online Submit Manuscripts Online

Michael Sinz
Bristol Myers Squibb
Wallingford, CT

View Full Editorial Board

Subscribe Purchase Articles Order Reprints

Current: 2.847
5 - Year: 3.222

Review and Research on Feature Selection Methods from NMR Data in Biological Fluids. Presentation of an Original Ensemble Method Applied to Atherosclerosis Field

Current Drug Metabolism, 15(5): 544-556.

Author(s): Nabil Semmar, Cecile Canlet, Bernadette Delplanque, Pascale Le Ruyet, Alain Paris and Jean-Charles Martin.

Affiliation: ISSBAT, 09 rue Zouhaier Essafi, 1006, Tunis, Tunisia.


Metabolic pools of biological matrices can be extensively analyzed by NMR. Measured data consist of hundreds of NMR signals with different chemical shifts and intensities representing different metabolites’ types and levels, respectively. Relevant predictive NMR signals need to be extracted from the pool using variable selection methods. This paper presents both a review and research on this metabolomics field. After reviews on discriminant potentials and statistical analyses of NMR data in biological fields, the paper presents an original approach to extract a small number of NMR signals in a biological matrix A (BM-A) in order to predict metabolic levels in another biological matrix B (BM-B). Initially, NMR dataset of BM-A was decomposed into several row-column homogeneous blocks using hierarchical cluster analysis (HCA). Then, each block was subjected to a complete set of Jackknifed correspondence analysis (CA) by removing separately each individual (row). Each CA condensed the numerous NMR signals into some principal components (PCs). The different PCs representing the (n – 1) active individuals were used as latent variables in a stepwise multi-linear regression to predict metabolic levels in BM-B. From the built regression model, metabolite level in the outside individual was predicted (for next model validation). From all the PCs-based regression models resulting from all the jackknifed CA applied on all the individuals, the most contributive NMR signals were identified by their highest absolute contributions to PCs. Finally, these selected NMR signals (measured in BMA) were used to build final population and sub-population regression models predicting metabolite levels in BM-B.


Aortic cholesteryl ester, cluster analysis, correspondence analysis, Jackknife technique, metabolomics, multiple linear regression, stepwise technique, urinary <sup>1</sup>H-NMR signals.

Purchase Online Order Reprints Order Eprints Rights and Permissions

Article Details

Volume: 15
Issue Number: 5
First Page: 544
Last Page: 556
Page Count: 13
DOI: 10.2174/1389200215666140505152333
Price: $58
12th annual ADMET 2017Global Biotechnology Congress 2017Drug Discovery and Therapy World Congress 2017

Related Journals

Related eBooks

Webmaster Contact: Copyright © 2017 Bentham Science