Vavilov Institute of General Genetics, Russian Academy of Sciences (RAS), 119333 Moscow, Russia.
Int J Mol Sci. 2023 Nov 17;24(22):16459. doi: 10.3390/ijms242216459.
In the last few years, investigation of the gut-brain axis and the connection between the gut microbiota and the human nervous system and mental health has become one of the most popular topics. Correlations between the taxonomic and functional changes in gut microbiota and major depressive disorder have been shown in several studies. Machine learning provides a promising approach to analyze large-scale metagenomic data and identify biomarkers associated with depression. In this work, machine learning algorithms, such as random forest, elastic net, and You Only Look Once (YOLO), were utilized to detect significant features in microbiome samples and classify individuals based on their disorder status. The analysis was conducted on metagenomic data obtained during the study of gut microbiota of healthy people and patients with major depressive disorder. The YOLO method showed the greatest effectiveness in the analysis of the metagenomic samples and confirmed the experimental results on the critical importance of a reduction in the amount of for the manifestation of depression. These findings could contribute to a better understanding of the role of the gut microbiota in major depressive disorder and potentially lead the way for novel diagnostic and therapeutic strategies.
在过去的几年中，对肠道-大脑轴以及肠道微生物群与人类神经系统和心理健康之间的联系的研究已成为最热门的话题之一。多项研究表明，肠道微生物群的分类和功能变化与重度抑郁症之间存在相关性。机器学习为分析大规模宏基因组数据和识别与抑郁相关的生物标志物提供了一种很有前途的方法。在这项工作中，使用了机器学习算法，如随机森林、弹性网络和 You Only Look Once (YOLO)，来检测微生物组样本中的显著特征，并根据个体的疾病状态进行分类。分析是在对健康人和重度抑郁症患者的肠道微生物群进行的宏基因组数据上进行的。YOLO 方法在宏基因组样本分析中表现出最大的有效性，并证实了实验结果，即 的减少对于抑郁表现的至关重要性。这些发现有助于更好地理解肠道微生物群在重度抑郁症中的作用，并可能为新的诊断和治疗策略开辟道路。
