UCL School of Pharmacy, University College London , London, UK.
FabRx Ltd., Ashford , Kent, UK.
Gut Microbes. 2021 Jan-Dec;13(1):1-20. doi: 10.1080/19490976.2021.1872323.
The last twenty years of seminal microbiome research has uncovered microbiota's intrinsic relationship with human health. Studies elucidating the relationship between an unbalanced microbiome and disease are currently published daily. As such, microbiome big data have become a reality that provide a mine of information for the development of new therapeutics. Machine learning (ML), a branch of artificial intelligence, offers powerful techniques for big data analysis and prediction-making, that are out of reach of human intellect alone. This review will explore how ML can be applied for the development of microbiome-targeted therapeutics. A background on ML will be given, followed by a guide on where to find reliable microbiome big data. Existing applications and opportunities will be discussed, including the use of ML to discover, design, and characterize microbiome therapeutics. The use of ML to optimize advanced processes, such as 3D printing and prediction of drug-microbiome interactions, will also be highlighted. Finally, barriers to adoption of ML in academic and industrial settings will be examined, concluded by a future outlook for the field.
过去二十年的开创性微生物组研究揭示了微生物组与人类健康的内在关系。目前每天都有研究阐明失衡的微生物组与疾病之间的关系。因此，微生物组大数据已经成为现实，为开发新疗法提供了丰富的信息来源。机器学习 (ML) 是人工智能的一个分支，为大数据分析和决策提供了强大的技术，单凭人类智力是无法企及的。本综述将探讨如何将 ML 应用于开发针对微生物组的疗法。首先介绍 ML 的背景，然后介绍在哪里可以找到可靠的微生物组大数据。将讨论现有的应用和机会，包括使用 ML 来发现、设计和表征微生物组疗法。还将强调使用 ML 来优化先进的工艺，如 3D 打印和预测药物-微生物组相互作用。最后，将检查 ML 在学术和工业环境中采用的障碍，并对该领域的未来前景进行展望。
