分类和回归机器学习模型用于预测抗生素和杀菌剂混合物的联合毒性和相互作用
Li-Tang Qin1, Jun-Yao Zhang2, Qiong-Yuan Nong2
1College of Environmental Science and Engineering, Guilin University of Technology, Guilin, 541004, China; Guangxi Key Laboratory of Environmental Pollution Control Theory and Technology, Guilin University of Technology, Guilin, 541004, China; Collaborative Innovation Center for Water Pollution Control and Water Safety in Karst Area, Guilin University of Technology, Guilin, 541004, China.
机器学习模型准确地预测了抗生素和杀菌剂混合物的毒性,这对于评估水生生态系统和Auxenochlorella pyrenoidosa等生物体的环境风险至关重要.
科学领域:
- 环境化学环境化学
- 生态毒理学 生态毒理学
- 计算生物学 计算生物学
背景情况:
- 抗生素和三类杀菌剂是常见的水生污染物.
- 它们的混合物对水生生态系统构成复杂的风险.
- 预测混合物毒性对于环境风险评估至关重要.
研究的目的:
- 为了评估75种抗生素和杀菌剂的二元混合物的毒性.
- 开发和比较机器学习模型来预测混合物毒性和分类相互作用 (协同作用/对抗作用).
- 评估对水生藻类Auxenochlorella pyrenoidosa的影响.
主要方法:
- 对75种二元混合物的毒性测试对Auxenochlorella pyrenoidosa进行毒性测试.
- 使用随机森林 (RF),K-最近邻居 (KNN) 和内核K-最近邻居 (KKNN) 的回归和分类模型的开发.
- 使用R2,Q2LOO,Q2F1-3和准确度等指标进行模型性能评估.
主要成果:
- 内核K-最近邻居 (KKNN) 模型显示出优异的回归性能 (R2 = 0.977,Q2LOO = 0.894).
- 随机森林 (RF) 模型作为分类器表现出色,在区分协同效应和对抗效应方面取得了高准确性 (测试组的0.905).
- 能够准确预测混合物毒性和相互作用类型.
结论:
- 机器学习模型,特别是KKNN和RF,是预测抗生素-杀菌剂混合物的毒性和分类相互作用的有效工具.
- 这些发现对于水生生态系统中化学混合物的环境风险评估至关重要.
- 该研究为了解和管理同时出现的污染物的生态影响提供了有价值的框架.
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