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论文类型：期刊论文

发表时间：2012-12-01

发表刊物：JOURNAL OF CHROMATOGRAPHY B-ANALYTICAL TECHNOLOGIES IN THE BIOMEDICAL AND LIFE SCIENCES

收录刊物：SCIE、EI、PubMed

卷号：910

期号：,SI

页面范围：149-155

ISSN号：1570-0232

关键字：Artificial contrast variables; Mutual information; SVM-RFE; Liver diseases; Metabolomics

摘要：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.