APA (7th ed.) Citation

Burgoon, L. D., Kluxen, F. M., & Frericks, M. (2023). Understanding and overcoming the technical challenges in using in silico predictions in regulatory decisions of complex toxicological endpoints – A pesticide perspective for regulatory toxicologists with a focus on machine learning models. Regulatory toxicology and pharmacology, 137, 105311. https://doi.org/10.1016/j.yrtph.2022.105311

Chicago Style (17th ed.) Citation

Burgoon, Lyle D., Felix M. Kluxen, and Markus Frericks. "Understanding and Overcoming the Technical Challenges in Using in Silico Predictions in Regulatory Decisions of Complex Toxicological Endpoints – A Pesticide Perspective for Regulatory Toxicologists with a Focus on Machine Learning Models." Regulatory Toxicology and Pharmacology 137 (2023): 105311. https://doi.org/10.1016/j.yrtph.2022.105311.

MLA (9th ed.) Citation

Burgoon, Lyle D., et al. "Understanding and Overcoming the Technical Challenges in Using in Silico Predictions in Regulatory Decisions of Complex Toxicological Endpoints – A Pesticide Perspective for Regulatory Toxicologists with a Focus on Machine Learning Models." Regulatory Toxicology and Pharmacology, vol. 137, 2023, p. 105311, https://doi.org/10.1016/j.yrtph.2022.105311.