Journal
JOURNAL OF CHROMATOGRAPHY B-ANALYTICAL TECHNOLOGIES IN THE BIOMEDICAL AND LIFE SCIENCES
Volume 910, Issue -, Pages 149-155Publisher
ELSEVIER
DOI: 10.1016/j.jchromb.2012.05.020
Keywords
Artificial contrast variables; Mutual information; SVM-RFE; Liver diseases; Metabolomics
Funding
- State Key Science & Technology Project for Infectious Diseases [2012ZX10002011]
- key foundation [20835006]
- National Natural Science Foundation of China [21021004]
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Filtering the discriminative metabolites from high dimension metabolome data is very important in metabolomics study. Support vector machine-recursive feature elimination (SVM-RFE) is an efficient feature selection technique and has shown promising applications in the analysis of the metabolome data. SVM-RFE measures the weights of the features according to the support vectors, noise and non-informative variables in the high dimension data may affect the hyper-plane of the SVM learning model. Hence we proposed a mutual information (MI)-SVM-RFE method which filters out noise and non-informative variables by means of artificial variables and MI, then conducts SVM-RFE to select the most discriminative features. A serum metabolomics data set from patients with chronic hepatitis B, cirrhosis and hepatocellular carcinoma analyzed by liquid chromatography-mass spectrometry (LC-MS) was used to demonstrate the validation of our method. An accuracy of 74.33 +/- 2.98% to distinguish among three liver diseases was obtained, better than 72.00 +/- 4.15% from the original SVM-RFE. Thirty-four ion features were defined to distinguish among the control and 3 liver diseases, 17 of them were identified. (C) 2012 Elsevier B.V. All rights reserved.
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