Automated machine learning in nanotoxicity assessment: A comparative study of predictive model performance.

ABSTRACT: Computational modeling has earned significant interest as an alternative to animal testing of toxicity assessment. However, the process of selecting an appropriate algorithm and fine-tuning hyperparameters for the developing of optimized models takes considerable time, expertise, and an intensive search. The recent emergence of automated machine learning (autoML) approaches, available as user-friendly platforms, has proven beneficial for individuals with limited knowledge in ML-based predictive model development. These autoML platforms automate crucial steps in model development, including data preprocessing, algorithm selection, and hyperparameter tuning. In this study, we used seven previously published and publicly available datasets for oxides and metals to develop nanotoxicity predict

SUBMITTER: Xiao X 

PROVIDER: S-EPMC10899003 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature