Machine Learning of Toxicological Big Data Enables Read-Across Structure Activity Relationships (RASAR) Outperforming Animal Test Reproducibility

Earlier we created a chemical hazard database via natural language processing of dossiers submitted to the European Chemical Agency with approximately 10 000 chemicals. We identified repeat OECD guideline tests to establish reproducibility of acute oral and dermal toxicity, eye and skin irritation,...

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Detalles Bibliográficos
Autores principales: Luechtefeld, Thomas, Marsh, Dan, Rowlands, Craig, Hartung, Thomas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2018
Materias:
Machine Learning for Read-across Toxicity Testing
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6135638/
https://www.ncbi.nlm.nih.gov/pubmed/30007363
http://dx.doi.org/10.1093/toxsci/kfy152

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6135638/
https://www.ncbi.nlm.nih.gov/pubmed/30007363
http://dx.doi.org/10.1093/toxsci/kfy152

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