可解释的机器学习模型用于识别关键的肠道微生物和代谢物 - - 与肌痛性骨髓炎相关的生物标志物
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.
Computational and structural biotechnology journal
|April 23, 2024
概括
机器学习模型现在可以通过分析肠道微生物和代谢物来预测肌痛性骨髓灰质炎 (MG). 这种方法提供了个性化的诊断见解,并识别了关键生物标志物,如Lachnospiraceae, inosine和methylhistidine.
科学领域:
- 微生物组研究的研究.
- 代谢学 代谢学 代谢学
- 机器学习在诊断中的应用.
背景情况:
- 肌痛性骨髓灰质炎 (MG) 的诊断标志物有限,需要新的诊断方法和个性化护理.
- 肠道微生物群的改变与MG的发病有关,但它们与代谢物和诊断潜力的相互作用仍未得到充分研究.
- 机器学习 (ML) 为整合复杂的生物数据以识别疾病生物标志物提供了一个有希望的途径.
研究的目的:
- 通过整合肠道微生物群和代谢物数据,开发一种可解释的ML模型来诊断MG.
- 确定与MG相关的关键微生物和代谢生物标志物.
- 提供个性化的诊断解释和洞察肠道-微生物-代谢物-MG轴.
主要方法:
- 从19名MG患者和10名对照人群收集了便样本,用于16S rRNA测序和非目标代谢分析.
- 利用一种可解释的ML模型,将SHapley添加式扩展 (SHAP) 纳入生物标志物识别和个性化预测中.
- 结合顶部安普利康显著变异 (ASV) 和代谢物以优化预测性能.
主要成果:
- 在MG患者和对照人群之间观察到便微生物代谢物组成的显著差异.
- 发现的关键细菌家族是拉克诺斯皮拉属和鲁米诺科卡属.
- 最好的预测特征包括Lachnospiraceae, inosine和methylhistidine,组合ML模型实现了卓越的性能.
结论:
- 可以解释的ML框架整合了肠道微生物群和代谢物数据,可以准确地诊断MG.
- 个性化的解释突出了MG患者个体微生物代谢物贡献.
- 开发的算法可以通过在线计算器访问,方便MG诊断查和个性化评估.
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