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Revealing metabolite biomarkers for acupuncture treatment by linear programming based feature selection
Wang,Yong1,4; Wu,Qiao-Feng2; Chen,Chen1; Wu,Ling-Yun1,4; Yan,Xian-Zhong3; Yu,Shu-Guang2; Zhang,Xiang-Sun1,4; Liang,Fan-Rong2
2012-07-16
Source PublicationBMC Systems Biology
ISSN1752-0509
Volume6Issue:Suppl 1
AbstractAbstractBackgroundAcupuncture has been practiced in China for thousands of years as part of the Traditional Chinese Medicine (TCM) and has gradually accepted in western countries as an alternative or complementary treatment. However, the underlying mechanism of acupuncture, especially whether there exists any difference between varies acupoints, remains largely unknown, which hinders its widespread use.ResultsIn this study, we develop a novel Linear Programming based Feature Selection method (LPFS) to understand the mechanism of acupuncture effect, at molecular level, by revealing the metabolite biomarkers for acupuncture treatment. Specifically, we generate and investigate the high-throughput metabolic profiles of acupuncture treatment at several acupoints in human. To select the subsets of metabolites that best characterize the acupuncture effect for each meridian point, an optimization model is proposed to identify biomarkers from high-dimensional metabolic data from case and control samples. Importantly, we use nearest centroid as the prototype to simultaneously minimize the number of selected features and the leave-one-out cross validation error of classifier. We compared the performance of LPFS to several state-of-the-art methods, such as SVM recursive feature elimination (SVM-RFE) and sparse multinomial logistic regression approach (SMLR). We find that our LPFS method tends to reveal a small set of metabolites with small standard deviation and large shifts, which exactly serves our requirement for good biomarker. Biologically, several metabolite biomarkers for acupuncture treatment are revealed and serve as the candidates for further mechanism investigation. Also biomakers derived from five meridian points, Zusanli (ST36), Liangmen (ST21), Juliao (ST3), Yanglingquan (GB34), and Weizhong (BL40), are compared for their similarity and difference, which provide evidence for the specificity of acupoints.ConclusionsOur result demonstrates that metabolic profiling might be a promising method to investigate the molecular mechanism of acupuncture. Comparing with other existing methods, LPFS shows better performance to select a small set of key molecules. In addition, LPFS is a general methodology and can be applied to other high-dimensional data analysis, for example cancer genomics.
DOI10.1186/1752-0509-6-S1-S15
Language英语
WOS IDBMC:10.1186/1752-0509-6-S1-S15
PublisherBioMed Central
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Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/264
Collection应用数学研究所
Corresponding AuthorWang,Yong; Zhang,Xiang-Sun; Liang,Fan-Rong
Affiliation1.Chinese Academy of Sciences; Academy of Mathematics and Systems Science
2.Chengdu University of Traditional Chinese Medicine; Acupuncture and Moxibustion College
3.National Center for Biomedical Analysis
4.Chinese Academy of Sciences; National Center for Mathematics and Interdisciplinary Sciences
Recommended Citation
GB/T 7714
Wang,Yong,Wu,Qiao-Feng,Chen,Chen,et al. Revealing metabolite biomarkers for acupuncture treatment by linear programming based feature selection[J]. BMC Systems Biology,2012,6(Suppl 1).
APA Wang,Yong.,Wu,Qiao-Feng.,Chen,Chen.,Wu,Ling-Yun.,Yan,Xian-Zhong.,...&Liang,Fan-Rong.(2012).Revealing metabolite biomarkers for acupuncture treatment by linear programming based feature selection.BMC Systems Biology,6(Suppl 1).
MLA Wang,Yong,et al."Revealing metabolite biomarkers for acupuncture treatment by linear programming based feature selection".BMC Systems Biology 6.Suppl 1(2012).
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