Application of machine learning to predict the inhibitory activity of organic chemicals on thyroid stimulating hormone receptor

Xu, Xiaotian; Wang, Chen; Gui, Bingxin; Yuan, Xiangyi; Li, Chao; Zhao, Yun-Tao; Martyniuk, Christopher J.; Su, Limin

doi:10.1016/j.envres.2022.113175

Cited by 7 publications

(1 citation statement)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, for the regression problem, the evaluation indexes of the model include: coefficient of determination (R 2 ) (Uddin et al 2022;Suvarna et al 2022), mean absolute error (MAE) (Zhang et al 2021a;Lamoureux et al 2021;Chang and Medford 2021), mean square error (MSE), root mean square error (RMSE) (Wei et al 2022;Kim et al 2021) and mean absolute percentage error (MAPE) (Mir et al 2022;Ke et al 2021b;Bhagat et al 2021). For classification problems, the evaluation indicators of the model are accuracy (Liu et al 2022d), error rate, recall rate (Somarowthu et al 2011), balanced F score (F1 score) (Avakyan et al 2022;Findlay et al 2018), receiver operating characteristic (ROC) curve (Xu et al 2022;Razavi-Termeh et al 2021) and the area under ROC curve (AUROC or AUC) (Wan et al 2022;Ding et al 2022;Cashman et al 2017).…”

Section: Evaluation Metrics For Machine Learningmentioning

confidence: 99%

Synthesis optimization and adsorption modeling of biochar for pollutant removal via machine learning

Zhang

Chen

et al. 2023

Biochar

View full text Add to dashboard Cite

Due to large specific surface area, abundant functional groups and low cost, biochar is widely used for pollutant removal. The adsorption performance of biochar is related to biochar synthesis and adsorption parameters. But the influence factor is numerous, the traditional experimental enumeration is powerless. In recent years, machine learning has been gradually employed for biochar, but there is no comprehensive review on the whole process regulation of biochar adsorbents, covering synthesis optimization and adsorption modeling. This review article systematically summarized the application of machine learning in biochar adsorbents from the perspective of all-round regulation for the first time, including the synthesis optimization and adsorption modeling of biochar adsorbents. Firstly, the overview of machine learning was introduced. Then, the latest advances of machine learning in biochar synthesis for pollutant removal were summarized, including prediction of biochar yield and physicochemical properties, optimal synthetic conditions and economic cost. And the application of machine learning in pollutant adsorption by biochar was reviewed, covering prediction of adsorption efficiency, optimization of experimental conditions and revelation of adsorption mechanism. General guidelines for the application of machine learning in whole-process optimization of biochar from synthesis to adsorption were presented. Finally, the existing problems and future perspectives of machine learning for biochar adsorbents were put forward. We hope that this review can promote the integration of machine learning and biochar, and thus light up the industrialization of biochar. Graphical Abstract

show abstract