Researches on the microbiome have been actively conducted worldwide and the results have shown human gut bacterial environment significantly impacts on immune system, psychological conditions, cancers, obesity, and metabolic diseases. Thanks to the development of sequencing technology, microbiome studies with large number of samples are eligible on an acceptable cost nowadays. Large samples allow analysis of more sophisticated modeling using machine learning approaches to study relationships between microbiome and various traits. This article provides an overview of machine learning methods for non-data scientists interested in the association analysis of microbiomes and host phenotypes. Once genomic feature of microbiome is determined, various analysis methods can be used to explore the relationship between microbiome and host phenotypes that include penalized regression, support vector machine (SVM), random forest, and artificial neural network (ANN). Deep neural network methods are also touched. Analysis procedure from environment setup to extract analysis results are presented with Python programming language.

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微生物组研究的机器学习方法

微生物组的研究已在全球范围内积极开展，结果表明人类肠道细菌环境对免疫系统，心理状况，癌症，肥胖症和代谢性疾病有重大影响。由于测序技术的发展，如今对具有大量样品的微生物组研究已经可以以可接受的费用获得资格。大样本可使用机器学习方法分析更复杂的建模，以研究微生物组与各种特征之间的关系。本文概述了对微生物组和宿主表型的关联分析感兴趣的非数据科学家的机器学习方法。一旦确定了微生物组的基因组特征，各种分析方法可用于探索微生物组与宿主表型之间的关系，包括罚分回归，支持向量机（SVM），随机森林和人工神经网络（ANN）。深度神经网络方法也被触及。使用Python编程语言介绍了从环境设置到提取分析结果的分析过程。