L 1 范数支持向量机在代谢组学中的应用
收稿日期: 2014-06-20
修回日期: 2015-01-12
网络出版日期: 2015-03-05
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
Received date: 2014-06-20
Revised date: 2015-01-12
Online published: 2015-03-05
Supported by
国家青年自然科学基金资助项目(21105115)．
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．
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 . DOI: 10.11938/cjmr20150108
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.
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