Institute of Digestive Disease and Department of Medicine and Therapeutics, State Key Laboratory of Digestive Disease, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong.
J Gastroenterol Hepatol. 2021 Apr;36(4):841-847. doi: 10.1111/jgh.15501.
Gut microbiota has been shown to associate with the development of gastrointestinal diseases. In the last decade, development in whole metagenome sequencing and 16S rRNA sequencing technology has dramatically accelerated the gut microbiome's research and revealed its association with gastrointestinal disorders. Because of high dimensionality and complexity's intrinsic data characteristics, traditional bioinformatical methods could only explain the most significant changes with limited prediction accuracy. In contrast, machine learning is the application of artificial intelligence that provides the computational systems to automatically learn and improve from experience (training cohort) without being explicitly programmed. It is thus capable of unwiring high dimensionality and complicated correlational hitches. With modern computation power, machine learning is widely utilized to analyze microorganisms related to disease onset and other clinical features. It could help explore and identify novel biomarkers or improve the accuracy rate of disease diagnostic. This review summarized the most recent research that utilized machine learning to reveal the role of gut microbiota in intestinal disorders.
肠道微生物群已被证明与胃肠道疾病的发展有关。在过去的十年中，全基因组测序和 16S rRNA 测序技术的发展极大地加速了肠道微生物组的研究，并揭示了其与胃肠道疾病的关联。由于其内在数据特征具有高度的维度和复杂性，传统的生物信息学方法只能解释最显著的变化，预测准确性有限。相比之下，机器学习是人工智能的应用，它提供了计算系统，可以自动从经验（训练队列）中学习和改进，而无需进行显式编程。因此，它能够解决高维度和复杂的关联问题。随着现代计算能力的提高，机器学习被广泛用于分析与疾病发作和其他临床特征相关的微生物。它可以帮助探索和识别新型生物标志物，或提高疾病诊断的准确率。这篇综述总结了最近利用机器学习揭示肠道微生物群在肠道疾病中的作用的研究。
