CAJ | 학술논문

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．
대사조학시관우생물체내원성대사물질적정체급기변화규률적과학，야시일개수거밀집형적연구영역，유차사득모식식별재대사수거처리중유중요작용．L1범수지지향량궤(L1-Norm Support Vector Machines, L1-norm SVMs)작위재모식식별영역중준학、은건적방법，재대사조학중적응용교소．해문응용L1-norm SVM방법대소서감염혈흡충후적대사수거진행료분석，분석결과현시L1-norm SVM재취류여특정선택방면구유우세，병표명타재대사조학영역적응용유착잠력화전경．
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.