Zhou Yi-Hui, Gallins Paul
Department of Biological Sciences, North Carolina State University, Raleigh, NC, United States.
Bioinformatics Research Center, North Carolina State University, Raleigh, NC, United States.
Front Genet. 2019 Jun 25;10:579. doi: 10.3389/fgene.2019.00579. eCollection 2019.
With the growing importance of microbiome research, there is increasing evidence that host variation in microbial communities is associated with overall host health. Advancement in genetic sequencing methods for microbiomes has coincided with improvements in machine learning, with important implications for disease risk prediction in humans. One aspect specific to microbiome prediction is the use of taxonomy-informed feature selection. In this review for non-experts, we explore the most commonly used machine learning methods, and evaluate their prediction accuracy as applied to microbiome host trait prediction. Methods are described at an introductory level, and R/Python code for the analyses is provided.
随着微生物组研究的重要性日益增加，越来越多的证据表明，微生物群落中的宿主差异与宿主整体健康状况相关。微生物组基因测序方法的进步与机器学习的改进同步出现，这对人类疾病风险预测具有重要意义。微生物组预测的一个特定方面是使用基于分类学的特征选择。在这篇面向非专家的综述中，我们探讨了最常用的机器学习方法，并评估了它们在微生物组宿主性状预测中的预测准确性。方法以入门级水平进行描述，并提供了分析用的R/Python代码。
