In silico prediction of toxic action mechanisms of phenols for imbalanced data with Random Forest learner

Author:

Chen Jing,Tang Yuan Yan,Fang Bin,Guo Chang

Publisher

Elsevier BV

Subject

Materials Chemistry,Computer Graphics and Computer-Aided Design,Physical and Theoretical Chemistry,Spectroscopy

Cited by 19 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Insights into the Luminescence Quantum Yields of Cyclometalated Iridium(III) Complexes: A Density Functional Theory and Machine Learning Approach;The Journal of Physical Chemistry A;2023-08-31

2. Data-Driven Quantitative Structure–Activity Relationship Modeling for Human Carcinogenicity by Chronic Oral Exposure;Environmental Science & Technology;2023-04-11

3. Prediction of Toxicity of Phenols Using Artificial Neural Networks;2022 12th International Conference on Advanced Computer Information Technologies (ACIT);2022-09-26

4. QSRR Approach: Application to Retention Mechanism in Liquid Chromatography;Novel Aspects of Gas Chromatography and Chemometrics [Working Title];2022-07-28

5. Classification of some chemical drugs by genetic algorithm and deep neural network hybrid method;Concurrency and Computation: Practice and Experience;2021-02-22

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