L 1 范数支持向量机在代谢组学中的应用

doi:10.11938/cjmr20150108

波谱学杂志 ›› 2015, Vol. 32 ›› Issue (1): 67-77.doi: 10.11938/cjmr20150108

L ₁ 范数支持向量机在代谢组学中的应用

丁国辉1,2,4，孙建强1,2,4，吴俊芳1,2,3，黄慎 1,2,4，丁义明1,2*

1. 中国科学院武汉物理与数学研究所，湖北武汉 430071;
2. 中国科学院生物磁共振分析重点实验室，武汉磁共振中心(中国科学院武汉物理与数学研究所)，湖北武汉 430071;
3. 波谱与原子分子物理国家重点实验室(中国科学院武汉物理与数学研究所)，湖北武汉 430071;
4. 中国科学院大学，北京 100049

收稿日期:2014-06-20 修回日期:2015-01-12 出版日期:2015-03-05 发布日期:2015-03-05
作者简介:丁国辉(1990-)，男，甘肃兰州人，硕士研究生，应用数学专业，从事统计学习与模式识别相关应用的研究． *通讯联系人：丁义明，电话：027-87199080，E-mail：ding@wipm.ac.cn.
基金资助:
国家青年自然科学基金资助项目(21105115)．

L₁-Norm Support Vector Machine and Its Application in Metabonomics

DING Guo-hui1,2,4，SUN Jian-qiang1,2,4，WU Jun-fang1,2,3，HUANG Shen1,2,4，DING Yi-ming1,2*

1. Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan 430071, China;
2. Key Laboratory of Magnetic Resonance in Biological Systems, Wuhan Center for Magnetic Resonance(Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences), Wuhan 430071, China;
3. State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics (Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences), Wuhan 430071, China;
4. University of Chinese Academy of Sciences, Beijing 100049, China

Received:2014-06-20 Revised:2015-01-12 Online:2015-03-05 Published:2015-03-05
About author:*Corresponding author：DING Yi-ming, Tei: +86-27-87199080, E-mail: ding@wipm.ac.cn.
Supported by:
国家青年自然科学基金资助项目(21105115)．

摘要/Abstract

摘要：

代谢组学是关于生物体内源性代谢物质的整体及其变化规律的科学，也是一个数据密集型的研究领域，由此使得模式识别在代谢数据处理中有重要作用．L₁ 范数支持向量机(L₁-Norm Support Vector Machines, L₁-norm SVMs)作为在模式识别领域中准确、稳健的方法，在代谢组学中的应用较少．该文应用L₁-norm SVM 方法对小鼠感染血吸虫后的代谢数据进行了分析，分析结果显示L1-norm SVM 在聚类与特征选择方面具有优势，并表明它在代谢组学领域的应用有着潜力和前景．

关键词: 模式识别, L₁ 范数支持向量机(L₁-norm SVM), 正交偏最小二乘(O-PLS), 代谢组学, 核磁共振(NMR)

Abstract:

Metabonomics analyzes metabolite profiles in living systems and its dynamic responses to changes of endogenous (i.e., physiology and development) and exogenous(i.e., environment and xenobiotics) factors. Pattern recognition plays an important role in data-processing in metabonomic. L₁-norm support vector machine (L₁-norm SVM) is an accurate and robust method in pattern recognition, but not widely used in metabonomics. In this study, we used L₁-norm SVM to analyze metabonomic data obtained from mice infected by schistosomiasis. It was shown that L₁-norm SVM had better performance than
orthogonal partial least squares (O-PLS) in terms of clustering and feature selection. The results also showed that support vector machines have great potential and prospects for data-processing in metabonomics.

Key words: pattern recognition, L₁-norm support vector machine, orthogonal partial least squares, metabonomics, nuclear magnetic resonance

中图分类号:

O482.53

丁国辉1,2,4，孙建强1,2,4，吴俊芳1,2,3，黄慎 1,2,4，丁义明1,2. L ₁ 范数支持向量机在代谢组学中的应用[J]. 波谱学杂志, 2015, 32(1): 67-77.

DING Guo-hui1,2,4，SUN Jian-qiang1,2,4，WU Jun-fang1,2,3，HUANG Shen1,2,4，DING Yi-ming1,2. L₁-Norm Support Vector Machine and Its Application in Metabonomics[J]. Chinese Journal of Magnetic Resonance, 2015, 32(1): 67-77.

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L ₁ 范数支持向量机在代谢组学中的应用

L₁-Norm Support Vector Machine and Its Application in Metabonomics

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