Cho Hyun-Woo, Kim Seoung Bum, Jeong Myong K, Park Youngja, Ziegler Thomas R, Jones Dean P
Department of Industrial and Information Engineering, The University of Tennessee, Knoxville, TN 37996, USA.
Expert Syst Appl. 2008 Oct 1;35(3):967-975. doi: 10.1016/j.eswa.2007.08.050.
High-resolution nuclear magnetic resonance (NMR) spectroscopy has provided a new means for detection and recognition of metabolic changes in biological systems in response to pathophysiological stimuli and to the intake of toxins or nutrition. To identify meaningful patterns from NMR spectra, various statistical pattern recognition methods have been applied to reduce their complexity and uncover implicit metabolic patterns. In this paper, we present a genetic algorithm (GA)-based feature selection method to determine major metabolite features to play a significant role in discrimination of samples among different conditions in high-resolution NMR spectra. In addition, an orthogonal signal filter was employed as a preprocessor of NMR spectra in order to remove any unwanted variation of the data that is unrelated to the discrimination of different conditions. The results of k-nearest neighbors and the partial least squares discriminant analysis of the experimental NMR spectra from human plasma showed the potential advantage of the features obtained from GA-based feature selection combined with an orthogonal signal filter.
高分辨率核磁共振（NMR）光谱为检测和识别生物系统中响应病理生理刺激以及毒素或营养物质摄入而发生的代谢变化提供了一种新方法。为了从NMR光谱中识别有意义的模式，已应用各种统计模式识别方法来降低其复杂性并揭示隐含的代谢模式。在本文中，我们提出了一种基于遗传算法（GA）的特征选择方法，以确定在高分辨率NMR光谱中不同条件下样本区分中起重要作用的主要代谢物特征。此外，采用正交信号滤波器作为NMR光谱的预处理程序，以去除与不同条件区分无关的任何不必要的数据变化。对来自人血浆的实验NMR光谱进行的k近邻和偏最小二乘判别分析结果表明，基于GA的特征选择结合正交信号滤波器所获得的特征具有潜在优势。
