Predicting nanotoxicity by an integrated machine learning and metabolomics approach

被引:26
作者
Peng, Ting [1 ]
Wei, Changhong [1 ]
Yu, Fubo [1 ]
Xu, Jing [1 ]
Zhou, Qixing [1 ]
Shi, Tonglei [1 ]
Hu, Xiangang [1 ]
机构
[1] Nankai Univ, Coll Environm Sci & Engn,Minist Educ, Key Lab Pollut Proc & Environm Criteria, Tianjin Key Lab Environm Remediat & Pollut Contro, Tianjin 300350, Peoples R China
基金
中国国家自然科学基金;
关键词
Nanotoxicity; Machine learning; Metabolic pathway; Metabolomics; Nanoparticles; Nanosafety;
D O I
10.1016/j.envpol.2020.115434
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Predicting the biological responses to engineered nanoparticles (ENPs) is critical to their environmental health assessment. The disturbances of metabolic pathways reflect the global profile of biological responses to ENPs but are difficult to predict due to the highly heterogeneous data from complicated biological systems and various ENP properties. Herein, integrating multiple machine learning models and metabolomics enabled accurate prediction of the disturbance of metabolic pathways induced by 33 ENPs. Screening nine typical properties of ENPs identified type and size as the top features determining the effects on metabolic pathways. Similarity network analysis and decision tree models overcame the highly heterogeneous data sources to visualize and judge the occurrence of metabolic pathways depending on the sorting priority features. The model accuracy was verified by animal experiments and reached 75%-100%, even for the prediction of ENPs outside of databases. The models also predicted metabolic pathway-related histopathology. This work provides an approach for the quick assessment of environmental health risks induced by known and unknown ENPs. (C) 2020 Elsevier Ltd. All rights reserved.
引用
收藏
页数:9
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