使用公开可用数据的口腔微生物组的多样性和随机森林模型
Alba Regueira-Iglesias1, Berta Suárez-Rodríguez1, Triana Blanco-Pintos1
1Oral Sciences Research Group, Special Needs Unit, Department of Surgery and Medical-Surgical Specialties, School of Medicine and Dentistry, Universidade de Santiago de Compostela, Instituto de Investigación Sanitaria de Santiago (IDIS), Santiago de Compostela, Spain.
Journal of periodontology
|August 21, 2025
概括
机器学习模型准确地区分了健康个体的口腔微生物群. 在唾液和牙斑中发现了特定的细菌特征,突出显示了牙周健康的潜在生物标志物.
科学领域:
- 口腔微生物组研究
- 微生物生态学
- 计算生物学
背景情况:
- 在牙周健康方面,关于上,下和唾液微生物组的16S元编码的证据有限.
- 了解这些微生物群落对于诊断和治疗至关重要.
研究的目的:
- 在牙周健康的受试者中分析上,下和唾液微生物群的多样性.
- 评估机器学习模型用于分类口腔微生物的潜力.
主要方法:
- 分析了来自491名健康受试者的848个样本 (上,下,唾液).
- 使用口腔特定数据库进行16S rRNA基因测序和处理.
- 随机森林 (RF) 模型建立并测试了分类准确性.
主要成果:
- 在斑块类型和唾液之间发现了显著的细菌丰度差异.
- 射频模型准确地按区分样本,具有优异的分辨能力 (AUC > 0.98).
- 五种安普利康序列变异 (ASV) 是对上和下斑块分类的关键 (AUC = 0. 908).
结论:
- 上和下细菌样本显示了适度的差异,但与唾液不同.
- 机器学习有效地识别出特定的微生物特征.
- 特定的ASV与下斑块和唾液有关,表明口腔的潜在生物标志物.
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