Machine-learning based prediction models for assessing skin irritation and corrosion potential of liquid chemicals using physicochemical properties by XGBoost-Reference-Cited by-同舟云学术

Machine-learning based prediction models for assessing skin irritation and corrosion potential of liquid chemicals using physicochemical properties by XGBoost

Published:2023-01-23 Issue:2 Volume:39 Page:295-305
ISSN:1976-8257
Container-title:Toxicological Research
language:en
Short-container-title:Toxicol Res.

Author:

Kang Yeonsoo,Kim Myeong Gyu,Lim Kyung-Min^ORCID

Funder

Ministry of Environment

Ministry of Health and Welfare

Publisher

Springer Science and Business Media LLC

Subject

Health, Toxicology and Mutagenesis,Toxicology

Link

https://link.springer.com/content/pdf/10.1007/s43188-022-00168-8.pdf

Reference44 articles.

1. Gallegos Saliner A, Tsakovska I, Pavan M, Patlewicz G, Worth AP, Research QiE (2007) Evaluation of SARs for the prediction of skin irritation/corrosion potential–structural inclusion rules in the BfR decision support system. SAR 18:331–342. https://doi.org/10.1080/10629360701304014

2. OECD (2015) Test guideline no. 404: acute dermal irritation/corrosion. OECD guidelines for the testing of chemicals. Organisation for Economic Cooperation and Development, Paris. https://doi.org/10.1787/9789264070622

3. OECD (2021) Test no. 439: in vitro skin irritation: reconstructed human epidermis test method. Organisation for Economic Cooperation and Development, Paris. https://doi.org/10.1787/20745788

4. OECD (2019) Test no. 431: In vitro skin corrosion: reconstructed human epidermis (RHE) test method. Organisation for Economic Cooperation and Development, Paris. https://doi.org/10.1787/20745788

5. Desprez B, Barroso J, Griesinger C, Kandárová H, Alépée N, Fuchs HW (2015) Two novel prediction models improve predictions of skin corrosive sub-categories by test methods of OECD test guideline no. 431. Toxicol In Vitro 29:2055–2080. https://doi.org/10.1016/j.tiv.2015.08.015

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