机器学习策略用于识别改变的肠道微生物组,用于在肌痛性硬化症的诊断查
Che-Cheng Chang1,2,3, Tzu-Chi Liu4, Chi-Jie Lu4,5,6
1PhD Program in Nutrition and Food Science, Fu Jen Catholic University, New Taipei City, Taiwan.
Frontiers in microbiology
|October 13, 2023
概括
研究人员在患有骨髓灰质炎 (MG) 的患者中发现了独特的肠道微生物组合. 使用31种关键肠道细菌的机器学习模型准确地识别了MG患者,这表明了非侵入性诊断生物标志物的潜力.
科学领域:
- 微生物组研究的研究.
- 自免疫性疾病机制 自免疫性疾病机制
- 计算生物学是一种计算生物学.
背景情况:
- 骨髓灰质炎 (MG) 是一种神经肌肉结合障碍,缺乏明显的生物标志物.
- 肠道微生物组的改变在自身免疫性疾病中很常见,这表明与MG的潜在联系.
研究的目的:
- 调查MG患者和健康个体之间的肠道微生物组合的差异.
- 开发一种机器学习模型,使用肠道微生物群数据进行MG的诊断查.
主要方法:
- 从MG患者和对照人群收集便样本进行DNA测序.
- 使用机器学习算法 (XGBoost,随机森林等) 分析安普利康序列变异 (ASV).
- 识别并对MG歧视具有重要意义的ASV (HIASV) 进行排名.
主要成果:
- 在MG和非MG受试者之间观察到肠道微生物组合的显著差异,特别是在Lachnospiraceae和Ruminococcaceae家族中.
- 在31个HIASV上训练的XGBoost模型在区分MG患者方面表现出高准确性.
- 在MG患者中,Lachnospiraceae的丰富程度与四肢衰弱的严重程度相关.
结论:
- 肠道微生物组的组成在MG和没有MG的个体之间有所不同.
- 使用特定肠道细菌 (HIASVs) 的机器学习模型显示了准确,非侵入性MG查的前景.
- 已识别的HIASV可以作为MG诊断和机理学研究的未来生物标志物.
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