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Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling

机译：机器学习方法用于代谢数据分析和代谢途径建模

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Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies.
机译：机器学习使用实验数据来优化样本或特征的聚类或分类，或者开发，扩充或验证可用于预测系统行为或属性的模型。预计机器学习将帮助从包括代谢组学数据在内的各种大数据以及新陈代谢模型的结果中提供可操作的知识。各种机器学习方法已应用于生物信息学和代谢分析，包括自组织图，支持向量机，内核机，贝叶斯网络或模糊逻辑。在较小程度上，机器学习也已被利用来利用基因组学和代谢组学数据的不断增长的可用性来优化代谢网络模型及其分析。在这种情况下，机器学习有助于代谢网络的发展，化学计量和动力学模型参数的计算以及模型中主要特征的分析，以优化生物反应器的应用。机器学习在代谢建模中的应用非常有趣（尽管非常复杂），这些示例将是本综述的主要重点，它将介绍几种不同类型的应用程序，用于模型优化，参数确定或使用模型进行系统分析，以及几种方法的利用不同类型的机器学习技术。

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