通过机器学习模型研究抗生素对环境微生物群的影响
Yiheng Du1, Khandaker Asif Ahmed2, Md Rakibul Hasan3,4
1Australian National University, Canberra, Australia.
IET systems biology
|March 28, 2025
概括
机器学习模型可以预测抗生素污染如何影响土壤微生物. 这些模型有助于理解微生物群落的变化以及土壤生态系统中抗生素耐药性的增加.
科学领域:
- 环境微生物学环境微生物学
- 计算生物学是一种计算生物学.
- 生态毒理学 生态毒理学
背景情况:
- 抗生素污染对土壤微生物群体构成重大威胁.
- 它可以导致微生物社区结构的改变和抗生素耐药细菌的出现.
研究的目的:
- 开发和评估机器学习 (ML) 模型,以评估抗生素对土壤微生物的影响.
- 预测微生物的数量和社区结构的变化,以应对抗生素暴露.
主要方法:
- 开发了三个ML模型:预测丰富的细菌类,预测丰富的抗生素效应,并预测短期数据的长期社区结构.
- 评估的模型包括随机森林和支持矢量机器 (SVM).
- 分析了用抗生素处理的环境土壤样本中的微生物丰度.
主要成果:
- 随机森林模型在预测细菌类 (模型1) 和稳定后的社区结构 (模型3) 中显示出高准确度.
- 随机森林和SVM模型在预测基于细菌丰度的抗生素治疗效果方面取得了高准确性 (近0.90).
结论:
- ML模型是研究抗生素污染对土壤微生物动态的影响的有效工具.
- 这些模型可以预测微生物的反应,有助于理解和管理抗生素耐药性.
- 该研究为环境微生物研究提供了一个计算框架.
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