Application of a Deep Neural Network to Metabolomics Studies and Its Performance in Determining Important Variables
Application of a Deep Neural Network to Metabolomics Studies and Its Performance in Determining Important Variables
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DOI：
10.1021/acs.analchem.7b03795
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发表时间：
2018-02-06
影响因子：
7.4
通讯作者：
Kikuchi, Jun
中科院分区：
文献类型：
--
作者：
Date, Yasuhiro;Kikuchi, Jun
深度神经网络(DNN)是一种机器学习方法，是分析来自生物和环境系统的大数据集的有力工具。然而，DNN不适用于代谢组学研究，因为它们在构建的分类和回归模型中难以识别贡献因素，例如生物标记物。在本文中，我们描述了一种改进的基于DNN的分析方法，该方法使用基于排列算法的平均减少精度(MDA)计算来估计每个变量的重要性；这种方法被称为DNN-MDA。DNN-MDA方法的性能是使用一组来自生活在日本各地不同河流的黄鳍虎鱼的代谢数据集进行评估的。将其性能与传统的多变量和机器学习方法进行了比较，发现DNN-MDA方法具有最好的分类精度(97.8%)。此外，DNN-MDA方法有助于确定重要的变量，如三甲胺N-氧化物、肌苷酸和甘氨酸，它们是有助于区分关东地区捕捞的鱼类和其他区域捕捞的鱼类的特征代谢物。因此，在生物和环境系统中进行的代谢组学研究中，DNN-MDA方法是确定标本的地理来源和确定其生物标志物的有用和强大的工具。
Deep neural networks (DNNs), which are kinds of the machine learning approaches, are powerful tools for analyzing big sets of data derived from biological and environmental systems. However, DNNs are not applicable to metabolomics studies because they have difficulty in identifying contribution factors, e.g., biomarkers, in constructed classification and regression models. In this paper, we describe an improved DNN-based analytical approach that incorporates an importance estimation for each variable using a mean decrease accuracy (MDA) calculation, which is based on a permutation algorithm; this approach is called DNN-MDA. The performance of the DNN-MDA approach was evaluated using a data set of metabolic profiles derived from yellowfin goby that lived in various rivers throughout Japan. Its performance was compared with that of conventional multivariate and machine learning methods, and the DNN-MDA approach was found to have the best classification accuracy (97.8%) among the examined methods. In addition to this, the DNN-MDA approach facilitated the identification of important variables such as trimethylamine N-oxide, inosinic acid, and glycine, which were characteristic metabolites that contributed to the discrimination of the geographical differences between fish caught in the Kanto region and those caught in other regions. As a result, the DNN-MDA approach is a useful and powerful tool for determining the geographical origins of specimens and identifying their biomarkers in metabolomics studies that are conducted in biological and environmental systems.