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
- DOI
- 10.1016/j.yrtph.2022.105311
- Published
- 2023-01
- Container
- Regulatory Toxicology and Pharmacology
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.yrtph.2022.105311,
title = {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},
author = {Lyle D. Burgoon and Felix M. Kluxen and Markus Frericks},
year = {2023},
journal = {Regulatory Toxicology and Pharmacology},
doi = {10.1016/j.yrtph.2022.105311},
url = {https://doi.org/10.1016/j.yrtph.2022.105311}
}RIS
TY - JOUR TI - 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 AU - Lyle D. Burgoon AU - Felix M. Kluxen AU - Markus Frericks PY - 2023 JO - Regulatory Toxicology and Pharmacology DO - 10.1016/j.yrtph.2022.105311 UR - https://doi.org/10.1016/j.yrtph.2022.105311 ER -
APA
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. https://doi.org/10.1016/j.yrtph.2022.105311
Source records
- crossref · retrieved 2026-09-25T19:09:23.395Z