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Characterization of Oxidative Damage Induced by Nanoparticles via Mechanism-driven Machine Learning Approaches by Xiaoqing Wang; Fei Li; Yuefa Teng; Chenglong Ji; Huifeng Wu is a Environmental Science article available to read on EtoBox.

What is Characterization of Oxidative Damage Induced by Nanoparticles via Mechanism-driven Machine Learning Approaches about?

The wide application of TiO2-based engineered nanoparticles (nTiO2) inevitably led to release into aquatic ecosystems. Importantly, increasing studies have emphasized the high risks of nTiO2 to coastal environments. Bivalves, the representative benthic filter feeders in coastal zones, acted as important roles to assess and monitor the toxic effects of nanoparticles. Oxidative damage was one of the main toxic mechanisms of nTiO2 on bivalves, but the experimental variables/nanomaterial characteristics were diverse and the toxicity mechanism was complex. Therefore, it was very necessary to develop machine learning model to characterize and predict the potential toxicity. In this study, thirty-six machine learning models were built by nanodescriptors combined with six machine learning algorithms. Among them, random forest (RF) – catalase (CAT), k-neighbors classifier (KNN) - glutathione peroxidase (GPx), neural networks - multilayer perceptron (ANN) – glutathione s-transferase (GST), random forest (RF) - malondialdehyde (MDA), random forest (RF) - reactive oxygen species (ROS), and extreme gradient boosting decision tree (XGB) - superoxide dismutase (SOD) models performed good with hig

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Author

Xiaoqing Wang; Fei Li; Yuefa Teng; Chenglong Ji; Huifeng Wu

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Environmental Science (Physical Sciences)