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学科主题: 分析化学
题名: A Modified SVM Method for Analyzing Metabonomics Data from HPLC-MS
作者: Lin XH(林晓惠) ;  Ruan Q(阮强) ;  KangYan ;  Chen SL(陈世礼) ;  Zhao XJ(赵欣捷)
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
出版日期: 2011
会议地点: 布达佩斯
通讯作者: 许国旺
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
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.
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.
内容类型: 会议论文
URI标识: http://cas-ir.dicp.ac.cn/handle/321008/116056
Appears in Collections:中国科学院大连化学物理研究所_会议论文

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Recommended Citation:
Lin XH,Ruan Q,KangYan,et al. A Modified SVM Method for Analyzing Metabonomics Data from HPLC-MS[C]. 见:36th International Symposium on High Performance Liquid Phase Separations and Related Techniques. 布达佩斯. 2011-6-19.
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