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题名: A support vector machine-recursive feature elimination feature selection method based on artificial contrast variables and mutual information
作者: Lin, Xiaohui2;  Yang, Fufang2;  Zhou, Lina1;  Yin, Peiyuan1;  Kong, Hongwei1;  Xing, Wenbin3;  Lu, Xin1;  Jia, Lewen4;  Wang, Quancai2;  Xu, Guowang1
关键词: Artificial contrast variables ;  Mutual information ;  SVM-RFE ;  Liver diseases ;  Metabolomics
刊名: JOURNAL OF CHROMATOGRAPHY B-ANALYTICAL TECHNOLOGIES IN THE BIOMEDICAL AND LIFE SCIENCES
发表日期: 2012-12-01
DOI: 10.1016/j.jchromb.2012.05.020
卷: 910, 页:149-155
收录类别: SCI
文章类型: Article
WOS标题词: Science & Technology ;  Life Sciences & Biomedicine ;  Physical Sciences
类目[WOS]: Biochemical Research Methods ;  Chemistry, Analytical
研究领域[WOS]: Biochemistry & Molecular Biology ;  Chemistry
英文摘要: 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.
关键词[WOS]: HEPATOMA PLASMA-MEMBRANES ;  MASS-SPECTROMETRY ;  MICROARRAY DATA ;  GENE SELECTION ;  SVM-RFE ;  HEPATOCELLULAR-CARCINOMA ;  REGENERATING LIVER ;  FATTY-ACIDS ;  L-CARNITINE ;  CLASSIFICATION
语种: 英语
WOS记录号: WOS:000312174700017
Citation statistics: 
内容类型: 期刊论文
URI标识: http://cas-ir.dicp.ac.cn/handle/321008/138056
Appears in Collections:中国科学院大连化学物理研究所_期刊论文

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作者单位: 1.Chinese Acad Sci, Dalian Inst Chem Phys, CAS Key Lab Separat Sci Analyt Chem, Dalian 116023, Peoples R China
2.Dalian Univ Technol, Sch Comp Sci & Technol, Dalian 116024, Peoples R China
3.Sixth Peoples Hosp, Dalian 116001, Peoples R China
4.Dalian Med Univ, Affiliated Hosp 1, Dept Nephrol, Dalian 116011, Peoples R China

Recommended Citation:
Lin, Xiaohui,Yang, Fufang,Zhou, Lina,et al. A support vector machine-recursive feature elimination feature selection method based on artificial contrast variables and mutual information[J]. JOURNAL OF CHROMATOGRAPHY B-ANALYTICAL TECHNOLOGIES IN THE BIOMEDICAL AND LIFE SCIENCES,2012,910:149-155.
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