机器学习方法用于预测抗生素耐药性基因在生物去除过程中的丰度变化
Tianyi Lu1, Jingfeng Gao1, Ke Zhang1
1National Engineering Laboratory for Advanced Municipal Wastewater Treatment and Reuse Technology, Department of Environmental Engineering, Beijing University of Technology, Beijing, 100124, China.
Journal of environmental management
|June 21, 2025
概括
污水处理厂可以传播抗生素耐药性基因 (ARG). 机器学习模型有效地预测生物去除过程中的ARG变化,识别控制策略的关键因素.
科学领域:
- 环境微生物学环境微生物学
- 废水工程 废水工程
- 计算生物学是一种计算生物学.
背景情况:
- 污水处理厂 (WWTP) 是抗菌剂 (AAs) 的储,增加了抗生素耐药性基因 (ARG) 传播的风险.
- 因素和微生物群落的复杂相互作用使得在生物去除 (BNR) 过程中追踪ARG命运具有挑战性.
研究的目的:
- 在BNR过程中应用机器学习 (ML) 模型来预测ARG和移动遗传元素 (MGE) 丰度变化.
- 确定影响ARG和MGE动态的关键因素.
- 开发数据驱动的策略,以减轻ARG传输风险.
主要方法:
- 四个机器学习模型,包括分类提升 (CatBoost) 和随机森林 (RF),用于预测ARG和MGE的丰度.
- 进行了特征重要性分析,以确定ARG和MGE变化的关键驱动因素.
- 部分依赖图 (PDP) 用于分析因素和丰度变化之间的关系.
主要成果:
- CatBoost模型在预测ARG丰度变化方面取得了高准确性 (R2 = 0.843),而RF模型在预测MGE丰度变化方面表现出色 (R2 = 0.708).
- 移动遗传元素 (MGE) 丰度是ARG丰度变化的最重要的预测因素.
- 微生物群落的组成 (Bacteroidetes,Proteobacteria) 和环境因素 (暴露时间,污染物度) 是关键的影响因素.
结论:
- 机器学习为评估和控制WWTP中的ARG传输风险提供了有效的方法.
- 了解MGE,微生物群落和环境因素的影响对于缓解ARG至关重要.
- 开发了一个用户友好的界面,以指导在BNR过程中优化ARG控制策略.
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