MetaPheno: A critical evaluation of deep learning and machine learning in metagenome-based disease prediction
MetaPheno: A critical evaluation of deep learning and machine learning in metagenome-based disease prediction
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DOI：
10.1016/j.ymeth.2019.03.003
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发表时间：
2019-08-15
期刊：
影响因子：
4.8
通讯作者：
Wang, Wei
中科院分区：
文献类型：
--
作者：
LaPierre, Nathan;Ju, Chelsea J. -T.;Wang, Wei
人类微生物组发挥着许多关键作用，影响着人类健康和福祉的几乎每一个方面。微生物组的状况与许多重大疾病有关。此外，测序技术的革命导致了公开测序数据的迅速增加。因此，在过去几年中，随着新方法的激增，人们越来越多地努力从宏基因组测序数据预测疾病状态。其中一些努力已经探索了利用一种强大的机器学习形式——深度学习，它已经成功地应用于几个生物领域。在这里，我们回顾了其中的一些方法和它们所基于的算法，特别关注深度学习方法。我们还使用各种机器学习和特征提取方法，对2型糖尿病和肥胖数据集进行了更深入的分析，这些数据集没有得到改进的结果。最后，我们提供了可能影响结果的研究设计考虑因素的观点，以及该领域可以采取的未来方向，以改善结果并提供更有价值的结论。本文中进行的分析的脚本和提取的特性可通过GitHub:https://github.com/nlapier2/metapheno获得。
The human microbiome plays a number of critical roles, impacting almost every aspect of human health and well-being. Conditions in the microbiome have been linked to a number of significant diseases. Additionally, revolutions in sequencing technology have led to a rapid increase in publicly-available sequencing data. Consequently, there have been growing efforts to predict disease status from metagenomic sequencing data, with a proliferation of new approaches in the last few years. Some of these efforts have explored utilizing a powerful form of machine learning called deep learning, which has been applied successfully in several biological domains. Here, we review some of these methods and the algorithms that they are based on, with a particular focus on deep learning methods. We also perform a deeper analysis of Type 2 Diabetes and obesity datasets that have eluded improved results, using a variety of machine learning and feature extraction methods. We conclude by offering perspectives on study design considerations that may impact results and future directions the field can take to improve results and offer more valuable conclusions. The scripts and extracted features for the analyses conducted in this paper are available via GitHub:https://github.com/nlapier2/metapheno.