基于舌膜微生物群的机器学习用于诊断消化系统瘤
Yubo Ma1, Zhengchen Jiang2,3, Yanan Wang2
1The Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, China.
Journal of oral microbiology
|April 10, 2025
概括
舌头涂层细菌可以帮助检测消化系统瘤 (DST). 机器学习模型在识别这些微生物特征以进行早期诊断方面表现出很高的准确性.
科学领域:
- 微生物组研究的研究.
- 在瘤学瘤学.
- 发现生物标志物的发现.
背景情况:
- 消化系统瘤 (DST) 由于模糊的症状,往往被诊断为晚期.
- 早期发现DST对于改善患者的治疗结果至关重要.
- 对于及时的DST诊断,需要使用非侵入性生物标志物.
研究的目的:
- 调查舌头涂层微生物群对DST的诊断潜力.
- 为了识别与DST相关的特定微生物特征.
- 评估机器学习模型在基于舌头微生物群的DST分类中的有效性.
主要方法:
- 从710名DST患者和489名健康对照 (HC) 收集了舌头涂层样本.
- 使用16S rRNA测序分析微生物组成.
- 应用了五个机器学习算法,包括XGBoost,以评估诊断性能.
主要成果:
- 与HC相比,DST患者的舌头涂层显著增加了微生物多样性.
- 确定了DST丰富的属 (Alloprevotella,Prevotella) 和HC主导的种类 (Neisseria,Haemophilus,Porphyromonas). 这些属包括:
- XGBoost模型在DST诊断中实现了0.926的AUC,超过了其他模型.
结论:
- 舌头涂层微生物群作为DST检测的非侵入性生物标志物具有前景.
- 机器学习模型,特别是XGBoost,可以有效地利用微生物数据进行DST诊断.
- 对舌头微生物群的进一步研究可能会导致改善对消化系统瘤的早期检测策略.
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