Issue 18, 2013
From the journal:

Analytical Methods

Interpretation of type 2 diabetes mellitus relevant GC-MS metabolomics fingerprints by using random forests†

Jian-Hua Huang,a   Hua-Lin Xie,ac   Jun Yan,a   Dong-Sheng Cao,a   Hong-Mei Lu,a   Qing-Song Xub  and  Yi-Zeng Liang*a  

* Corresponding authors

a Research Center of Modernization of Traditional Chinese Medicines, Department of Chemistry and Chemical Engineering, Central South University, Changsha 410083, P. R. China
E-mail: yizeng_liang@263.net
Fax: +86-731-88830831
Tel: +86-731-88830831

b School of Mathematical Sciences and Computing Technology, Central South University, Changsha 410083, P. R. China

c School of Chemistry and Chemical Engineering, Yangtze Normal University, Fuling 408100, P. R. China

Abstract

In this study, we discussed the application of random forest (RF) methods for extracting relevant biological knowledge from two type 2 diabetes mellitus (T2DM) relevant metabolomics fingerprinting experiments. The models constructed by RF could visually discriminate type 2 diabetic mice from a healthy control group and represent the variance of metabolic profiles of diabetic mice in the therapeutic process with repaglinide. Simultaneously, some informative metabolites have been successfully discovered by means of variable importance ranking in the RF program. The current research demonstrated that RF was a versatile classification algorithm, which was suitable for the analysis of complex metabolomics data and would be a complement or an alternative to pathogenesis and pharmacodynamics research.

Graphical abstract: Interpretation of type 2 diabetes mellitus relevant GC-MS metabolomics fingerprints by using random forests

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Article information

DOI
https://doi.org/10.1039/C3AY40379C
Article type
Paper
Submitted
08 Mar 2013
Accepted
28 Jun 2013
First published
02 Jul 2013

Anal. Methods, 2013,5, 4883-4889

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Interpretation of type 2 diabetes mellitus relevant GC-MS metabolomics fingerprints by using random forests

J. Huang, H. Xie, J. Yan, D. Cao, H. Lu, Q. Xu and Y. Liang, Anal. Methods, 2013, 5, 4883 DOI: 10.1039/C3AY40379C

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