Ramadan Z, Jacobs D, Grigorov M, Kochhar S
Nestlé Research Center, Vers-chez-les-Blanc, CH-1000 Lausanne 26, Switzerland.
Talanta. 2006 Feb 28;68(5):1683-91. doi: 10.1016/j.talanta.2005.08.042. Epub 2005 Sep 19.
The aim of this study was to evaluate evolutionary variable selection methods in improving the classification of (1)H nuclear magnetic resonance (NMR) metabonomic profiles, and to identify the metabolites that are responsible for the classification. Human plasma, urine, and saliva from a group of 150 healthy male and female subjects were subjected to (1)H NMR-based metabonomic analysis. The (1)H NMR spectra were analyzed using two pattern recognition methods, principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA), to identify metabolites responsible for gender differences. The use of genetic algorithms (GA) for variable selection methods was found to enhance the classification performance of the PLS-DA models. The loading plots obtained by PCA and PLS-DA were compared and various metabolites were identified that are responsible for the observed separations. These results demonstrated that our approach is capable of identifying the metabolites that are important for the discrimination of classes of individuals of similar physiological conditions.
本研究的目的是评估进化变量选择方法在改善基于氢核磁共振（NMR）代谢组学谱分类方面的效果，并识别出对分类起作用的代谢物。对一组150名健康男性和女性受试者的人血浆、尿液和唾液进行了基于氢核磁共振的代谢组学分析。使用主成分分析（PCA）和偏最小二乘判别分析（PLS-DA）这两种模式识别方法对氢核磁共振谱进行分析，以识别导致性别差异的代谢物。研究发现，使用遗传算法（GA）作为变量选择方法可提高PLS-DA模型的分类性能。比较了通过PCA和PLS-DA获得的载荷图，并识别出了导致观察到的分离现象的各种代谢物。这些结果表明，我们的方法能够识别出对于区分相似生理条件个体类别很重要的代谢物。
