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

Author:

Qin Li-Tang,Zhang Jun-Yao,Nong Qiong-Yuan,Xu Xia-Chang-Li,Zeng Hong-Hu,Liang Yan-Peng,Mo Ling-Yun

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference62 articles.

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2. First report of q-RASAR modeling toward an approach of easy interpretability and efficient transferability;Banerjee;Mol. Divers.,2022

3. On some novel similarity-based functions used in the ML-based q-RASAR approach for efficient quantitative predictions of selected toxicity end points;Banerjee;Chem. Res. Toxicol.,2023

4. ARKA: a framework of dimensionality reduction for machine-learning classification modeling, risk assessment, and data gap-filling of sparse environmental toxicity data [10.1039/D4EM00173G];Banerjee;Environ. Sci. J. Integr. Environ. Res.: Process. Impacts,2024

5. The enhanced lipid productivity of Chlorella minutissima and Chlorella pyrenoidosa by carbon coupling nitrogen manipulation for biodiesel production;Bharte;Environ. Sci. Pollut. Res.,2019

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