Machine learning-based prediction of fish acute mortality: implementation, interpretation, and regulatory relevance-Reference-Cited by-同舟云学术

Machine learning-based prediction of fish acute mortality: implementation, interpretation, and regulatory relevance

Published:2024 Issue:8 Volume:3 Page:1124-1138
ISSN:2754-7000
Container-title:Environmental Science: Advances
language:en
Short-container-title:Environ. Sci.: Adv.

Author:

Gasser Lilian¹^ORCID,Schür Christoph²^ORCID,Perez-Cruz Fernando¹³^ORCID,Schirmer Kristin²⁴^ORCID,Baity-Jesi Marco²^ORCID

Affiliation:

1. Swiss Data Science Center (SDSC), Zürich, Switzerland

2. Eawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland

3. ETH Zürich: Department of Computer Science, Zürich, Switzerland

4. ETH Zürich: Department of Environmental Systems Science, Zürich, Switzerland

Abstract

The study focuses on the implementation and interpretation of four state-of-the-art machine learning methods coupled with six molecular representations to predict fish acute mortality.

Funder

Horizon 2020 Framework Programme

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/VA/D4VA00072B

Reference62 articles.

1. EC – European Commission , Regulation (EC) No 1907/2006 of the European Parliament and of the Council of 18 December 2006 Concerning the Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH), Establishing a European Chemicals Agency, Amending Directive 1999/45/EC and Repealing Council Regulation (EEC) No 793/93 and ComMission Regulation (EC) No 1488/94 as Well as Council DiRective 76/769/EEC and Commission Directives 91/155/EEC, 93/67/EEC, 93/105/EC and 2000/21/EC , 2006

2. OECD , Test No. 203: Fish, Acute Toxicity Test , Organisation for Economic Cooperation and Development , Paris , 2019

3. K.Mittal , D.Crump , J. A.Head , M.Hecker , G.Hickey , S.Maguire , N.Hogan , J.Xia and N.Basu , Resource Requirements for Ecotoxicity Testing: A Comparison of Traditional and New Approach Methods , BioRxiv , 2022 , preprint, 10.1101/2022.02.24.481630

4. ToxAIcology - The evolving role of artificial intelligence in advancing toxicology and modernizing regulatory science

5. QSAR without borders