Classification and regression machine learning models for predicting the combined toxicity and interactions of antibiotics and fungicides mixtures.

Journal: Environmental pollution (Barking, Essex : 1987)
PMID: 39033842

Abstract

Antibiotics and triazole fungicides coexist in varying concentrations in natural aquatic environments, resulting in complex mixtures. These mixtures can potentially affect aquatic ecosystems. Accurately distinguishing synergistic and antagonistic mixtures and predicting mixture toxicity are crucial for effective mixture risk assessment. We tested the toxicities of 75 binary mixtures of antibiotics and fungicides against Auxenochlorella pyrenoidosa. Both regression and classification models for these mixtures were developed using machine learning models: random forest (RF), k-nearest neighbors (KNN), and kernel k-nearest neighbors (KKNN). The KKNN model emerged as the best regression model with high values of determination coefficient (R = 0.977), explained variance in prediction leave-one-out (Q = 0.894), and explained variance in external prediction (Q = 0.929, Q = 0.929, and Q = 0.923). The RF model, the leading classifier, exhibited high accuracy (accuracy = 1 for the training set and 0.905 for the test set) in distinguishing the synergistic and antagonistic mixtures. These results provide crucial value for the risk assessment of mixtures.

Authors

  • Li-Tang Qin
    College 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.
  • Jun-Yao Zhang
    College of Environmental Science and Engineering, Guilin University of Technology, Guilin, 541004, China.
  • Qiong-Yuan Nong
    College of Environmental Science and Engineering, Guilin University of Technology, Guilin, 541004, China.
  • Xia-Chang-Li Xu
    College of Environmental Science and Engineering, Guilin University of Technology, Guilin, 541004, China.
  • Hong-Hu Zeng
    College 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.
  • Yan-Peng Liang
    College 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. Electronic address: liangyanpeng@glut.edu.cn.
  • Ling-Yun Mo
    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; Technical Innovation Center of Mine Geological Environmental Restoration Engineering in Southern Karst Area, Nanjing, China. Electronic address: molingyun123@126.com.

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