使用多种生物标志物和早期风险预警系统对杀菌剂生态毒性的机器学习评估
Litang Qin1, Sihui Hao1, Lu Rong1
1College of Environmental Science and Engineering, Guilin University of Technology, Guilin 541004, China.
Environment international
|January 24, 2026
概括
机器学习模型使用30个生物标志物预测杀菌剂生态毒性. 麦隆迪化物 (MDA) 和活性氧物种 (ROS) 是土壤和水环境中早期风险评估的关键指标.
科学领域:
- 环境毒理学环境毒理学
- 生态毒理学 生态毒理学
- 计算毒理学计算毒理学
背景情况:
- 广泛使用真菌杀菌剂带来了生态毒理学风险.
- 现有的研究往往只关注单个化合物,物种或终点.
- 需要采取全面的方法来评估各种生物和环境中的杀菌剂风险.
研究的目的:
- 开发可解释的机器学习模型,用于评估土壤和水生生物的杀菌剂毒性.
- 确定能预测真菌杀菌剂生态毒性的关键机械生物标志物.
- 为积极的环境管理创建一个早期风险预警工具.
主要方法:
- 使用21个算法和30个机械生物标志物 (9个土壤,21个水生) 构建了672个分类模型.
- 采用渐变增强和基于树的方法,通过内部 (交叉验证,引导) 和外部 (独立测试集) 方法验证.
- 利用夏普利添加剂解释 (SHAP) 来识别有影响力的生物标志物.
主要成果:
- 渐变增强和基于树的模型实现了高的预测准确性 (ROC-AUC 0.990-1,000训练,0.861-1,000测试).
- 马隆迪化物 (MDA) 和活性氧物种 (ROS) 被确定为最重要的生物标志物.
- 基于MDA和ROS,开发了一个功能性早期风险预警工具.
结论:
- 通过机器学习建立了一个强大的,以机制为导向的框架,用于使用杀菌剂生态毒性评估.
- 证明了关键生物标志物 (MDA,ROS) 对预测生态风险的有用性.
- 为主动的早期预警系统和有针对性的杀菌剂管理策略提供了科学基础.
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