新生儿肠道微生物群的分层和SCFA相关微生物子组的识别,使用无监督聚类和机器学习分类
Payam Hosseinzadeh Kasani1, Cheol-Heui Yun2,3,4, Kee Hyun Cho1
1Department of Pediatrics, Kangwon National University Hospital, Kangwon National University School of Medicine, Chuncheon, Republic of Korea.
Frontiers in microbiology
|December 22, 2025
概括
这项研究使用机器学习对与短链脂肪酸 (SCFA) 生产相关的新生儿肠道微生物群落进行了分类. 结果揭示了不同的微生物子组,并突出了随机森林模型对婴儿健康的预测能力.
科学领域:
- 微生物组研究 微生物组研究
- 婴儿健康 婴儿健康
- 代谢分析分析 (Metabolic Profiling) 是一种对代谢进行分析的方法.
背景情况:
- 新生儿肠道微生物组对婴儿健康至关重要,通过短链脂肪酸 (SCFA) 生产影响它.
- 新生儿中SCFA产生微生物群落的精确组织尚不清楚.
- 了解这些社区对于早期健康干预至关重要.
研究的目的:
- 在新生儿肠道微生物群中分类不同的微生物亚群,与SCFA生产相关.
- 描述这些SCFA生产子组的组成和代谢潜力.
- 评估用于预测SCFA相关微生物群的机器学习模型.
主要方法:
- 招募了71对母婴对,收集了微生物16S rRNA基因测序和SCFA量化mekonium样本.
- 应用了无监督的集群算法 (K-Means,聚合,光谱,高斯混合模型) 来识别微生物子组.
- 利用随机森林和物流回归与ROC曲线来分类SCFA相关的集群.
主要成果:
- 聚合集群确定了不同的SCFA生产子组,其中集群1富含*Bacteroides*,*Prevotella*和*Enterococcus*,显示较高的SCFA.
- 第三个群体显示了中间代谢特征,表明功能连续性.
- 随机森林模型在分类SCFA相关的微生物集群方面表现出高准确性 (高达92.98%).
结论:
- 无监督的集群和机器学习有效地预测新生儿中SCFA相关的微生物子组.
- 这种方法提供了对早期代谢健康和微生物社区组织的见解.
- 未来的研究可以利用这些方法进行纵向跟踪和功能性基因组集成.
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