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题名: A method for handling metabonomics data from liquid chromatography/mass spectrometry: combinational use of support vector machine recursive feature elimination, genetic algorithm and random forest for feature selection
作者: Lin, Xiaohui1;  Wang, Quancai1;  Yin, Peiyuan2;  Tang, Liang3;  Tan, Yexiong3;  Li, Hong1;  Yan, Kang1;  Xu, Guowang2
关键词: Support vector machine ;  Genetic algorithm ;  Random forest ;  Liver diseases ;  Metabonomics ;  Metabolomics
刊名: METABOLOMICS
发表日期: 2011-12-01
DOI: 10.1007/s11306-011-0274-7
卷: 7, 期:4, 页:549-558
收录类别: SCI
文章类型: Article
WOS标题词: Science & Technology ;  Life Sciences & Biomedicine
类目[WOS]: Endocrinology & Metabolism
研究领域[WOS]: Endocrinology & Metabolism
英文摘要: Metabolic markers are the core of metabonomic surveys. Hence selection of differential metabolites is of great importance for either biological or clinical purpose. Here, a feature selection method was developed for complex metabonomic data set. As an effective tool for metabonomics data analysis, support vector machine (SVM) was employed as the basic classifier. To find out meaningful features effectively, support vector machine recursive feature elimination (SVM-RFE) was firstly applied. Then, genetic algorithm (GA) and random forest (RF) which consider the interaction among the metabolites and independent performance of each metabolite in all samples, respectively, were used to obtain more informative metabolic difference and avoid the risk of false positive. A data set from plasma metabonomics study of rat liver diseases developed from hepatitis, cirrhosis to hepatocellular carcinoma was applied for the validation of the method. Besides the good classification results for 3 kinds of liver diseases, 31 important metabolites including lysophosphatidylethanolamine (LPE) C16:0, palmitoylcarnitine, lysophosphatidylethanolamine (LPC) C18:0 were also selected for further studies. A better complementary effect of the three feature selection methods could be seen from the current results. The combinational method also represented more differential metabolites and provided more metabolic information for a "global" understanding of diseases than any single method. Further more, this method is also suitable for other complex biological data sets.
关键词[WOS]: VARIABLE IMPORTANCE MEASURES ;  MASS-SPECTROMETRY ;  EXPRESSION DATA ;  CLASSIFICATION ;  METABOLOMICS ;  BIOMARKERS ;  HEPATITIS ;  DISCOVERY
语种: 英语
WOS记录号: WOS:000295991900009
Citation statistics: 
内容类型: 期刊论文
URI标识: http://cas-ir.dicp.ac.cn/handle/321008/142714
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.Dalian Univ Technol, Sch Comp Sci & Technol, Dalian 116024, Peoples R China
2.Chinese Acad Sci, Dalian Inst Chem Phys, CAS Key Lab Separat Sci Analyt Chem, Dalian 116023, Peoples R China
3.Second Mil Med Univ, Eastern Hepatobiliary Surg Inst, Int Cooperat Lab Signal Transduct, Shanghai, Peoples R China

Recommended Citation:
Lin, Xiaohui,Wang, Quancai,Yin, Peiyuan,et al. A method for handling metabonomics data from liquid chromatography/mass spectrometry: combinational use of support vector machine recursive feature elimination, genetic algorithm and random forest for feature selection[J]. METABOLOMICS,2011,7(4):549-558.
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