机器学习的可行性研究,以探索全球废水处理厂污泥中的抗菌素耐药性和微生物社区结构之间的关系
Yi Li1, Cuicui Tao1, Shuyin Li1
1School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, China.
Bioresource technology
|November 27, 2024
概括
废水污泥含有抗生素耐药性基因 (ARG). 可解释的机器学习确定了Pseudomonadota细菌是这些环境中抗菌素耐药性 (AMR) 的关键贡献者,提供了一个有前途的分析工具.
科学领域:
- 环境微生物学环境微生物学
- 基因组学就是基因组学.
- 计算生物学是一种计算生物学.
背景情况:
- 废水污泥 (WSs) 是抗生素耐药性基因 (ARG) 的重要储存和排放来源.
- 了解WS中与抗菌素耐药性 (AMR) 相关的宿主细菌对于评估AMR形成和减轻生态风险至关重要.
研究的目的:
- 研究特定抗微生物耐药性 (AMR) 与废水污泥中的细菌分布之间的关系.
- 评估传统的相关性分析与机器学习方法在识别AMR相关细菌方面的有效性.
主要方法:
- 分析了来自中国江苏省污水处理厂的24个污泥样本和1559个公共污泥基因组数据集.
- 应用Procrustes和Spearman的相关性分析.
- 利用可解释的机器学习 (EML),特别是夏普利添加式扩展 (SHAP),来分析AMR-细菌的关联.
主要成果:
- 普罗克鲁斯特斯和斯皮尔曼的相关性分析给出了不满意的结果,p值>0.05和弱相关系数 (r < 0.8).
- 可解释的机器学习 (SHAP) 确定了Pseudomonadota作为废水污泥中AMR的主要贡献者 (39.3%74.2%).
- 与传统方法相比,机器学习在发现复杂的AMR-细菌关系方面表现优异.
结论:
- 可解释的机器学习,特别是SHAP,是分析复杂环境矩阵 (如废水污泥) 中AMR-细菌关系的强大而有前途的工具.
- 强烈建议将机器学习作为一种与传统方法并行的补充分析工具进行整合,以全面了解抗药性反应动态.
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