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学科主题分析化学
A Modified SVM Method for Analyzing Metabonomics Data from HPLC-MS
Lin XH(林晓惠); Ruan Q(阮强); KangYan; Chen SL(陈世礼); Zhao XJ(赵欣捷); Xu GW(许国旺)
会议文集36th International Symposium on High Performance Liquid Phase Separations and Related Techniques
会议名称36th International Symposium on High Performance Liquid Phase Separations and Related Techniques
会议日期2011-6-19
2011
会议地点布达佩斯
页码287-0
出版者待补充
出版地待补充
合作性质墙报
部门归属1808
主办者Hungarian Society for Separation Sciences
英文摘要Liquid chromatography-mass spectrometry (HPLC-MS) is an effective analytical technique which has been used in many applications, such as proteomics and metabolomics. Since the data produced by HPLC-MS usually contain hundreds (or even more) of variables including noisy and nonrelated information, selecting meaningful information from the data becomes quite critic. Support vector machine recursive feature elimination (SVM-RFE) is a very popular feature selection technique which is based on support vector machine (SVM). It has been successfully applied in analyzing biological data. In SVM-RFE, Filter-out-Factor (m), the number of the bottom ranked features to be deleted in each loop, can influence the performance of the algorithm. Different m results in the different selected feature subsets, hence the performances of the corresponding SVM classification models are quite different. In order to produce a stable result in processing high dimensional HPLC-MS data, we proposed an improved SVM-RFE method based on the dynamic Filter-out-Factor (SVM-RFE-DFF). In each loop, only the features lying in a specific window and having no contribution to improving the classification performance are eliminated. To show the usefulness of our new SVM-RFEDFF method we applied it to process metabonomics data of metabolic syndrome and liver diseases from UPLC/Q-TOF MS platform. Results showed that the SVM-RFE-DFF outperforms SVM-RFE in discriminating the patients from healthy controls.; Liquid chromatography-mass spectrometry (HPLC-MS) is an effective analytical technique which has been used in many applications, such as proteomics and metabolomics. Since the data produced by HPLC-MS usually contain hundreds (or even more) of variables including noisy and nonrelated information, selecting meaningful information from the data becomes quite critic. Support vector machine recursive feature elimination (SVM-RFE) is a very popular feature selection technique which is based on support vector machine (SVM). It has been successfully applied in analyzing biological data. In SVM-RFE, Filter-out-Factor (m), the number of the bottom ranked features to be deleted in each loop, can influence the performance of the algorithm. Different m results in the different selected feature subsets, hence the performances of the corresponding SVM classification models are quite different. In order to produce a stable result in processing high dimensional HPLC-MS data, we proposed an improved SVM-RFE method based on the dynamic Filter-out-Factor (SVM-RFE-DFF). In each loop, only the features lying in a specific window and having no contribution to improving the classification performance are eliminated. To show the usefulness of our new SVM-RFEDFF method we applied it to process metabonomics data of metabolic syndrome and liver diseases from UPLC/Q-TOF MS platform. Results showed that the SVM-RFE-DFF outperforms SVM-RFE in discriminating the patients from healthy controls.
文献类型会议论文
条目标识符http://cas-ir.dicp.ac.cn/handle/321008/116056
专题中国科学院大连化学物理研究所
通讯作者Xu GW(许国旺)
推荐引用方式
GB/T 7714
Lin XH,Ruan Q,KangYan,et al. A Modified SVM Method for Analyzing Metabonomics Data from HPLC-MS[C]. 待补充:待补充,2011:287-0.
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