Potential for Machine Learning to Address Data Gaps in Human Toxicity and Ecotoxicity Characterization-Reference-Cited by-同舟云学术

Potential for Machine Learning to Address Data Gaps in Human Toxicity and Ecotoxicity Characterization

Published:2023-11-01 Issue:46 Volume:57 Page:18259-18270
ISSN:0013-936X
Container-title:Environmental Science & Technology
language:en
Short-container-title:Environ. Sci. Technol.

Author:

von Borries Kerstin¹^ORCID,Holmquist Hanna²,Kosnik Marissa¹,Beckwith Katie V.³,Jolliet Olivier¹,Goodman Jonathan M.³^ORCID,Fantke Peter¹^ORCID

Affiliation:

1. Quantitative Sustainability Assessment, Department of Environmental and Resource Engineering, Technical University of Denmark, Bygningstorvet 115, 2800 Kgs. Lyngby, Denmark

2. IVL Swedish Environmental Research Institute, Aschebergsgatan 44, 411 33 Göteborg, Sweden

3. Centre for Molecular Informatics, Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom

Funder

HORIZON EUROPE Framework Programme

Stiftelsen f?r Milj?strategisk Forskning

Publisher

American Chemical Society (ACS)

Subject

Environmental Chemistry,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.est.3c05300

Reference73 articles.

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4. US EPA. Science Policy Council Handbook: Risk Characterization. 2000. www.epa.gov (accessed 2023-01-30).

5. European Commission. European Union System for the Evaluation of Substances 2.0 (EUSES 2.0); 2004. by the National Institute of Public Health and the Environment (RIVM), Bilthoven, The Netherlands (RIVM Report no. 601900005). Available via the European Chemicals Bureau, https://www.echa.europa.eu/documents/10162/28293536/euses_2-1_background_document_en.pdf/51d73868-784b-1476-f201-8a4ba17e78e0 (accessed 2023-01-30).

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