Development of QSAR models using artificial neural network analysis for risk assessment of repeated-dose, reproductive, and developmental toxicities of cosmetic ingredients-Reference-Cited by-同舟云学术

Development of QSAR models using artificial neural network analysis for risk assessment of repeated-dose, reproductive, and developmental toxicities of cosmetic ingredients

Published:2015 Issue:2 Volume:40 Page:163-180
ISSN:0388-1350
Container-title:The Journal of Toxicological Sciences
language:en
Short-container-title:J. Toxicol. Sci.

Author:

Hisaki Tomoka¹,Aiba née Kaneko Maki¹,Yamaguchi Masahiko¹,Sasa Hitoshi¹,Kouzuki Hirokazu¹

Affiliation:

1. Shiseido Research Center, Shiseido Co. Ltd.

Publisher

Japanese Society of Toxicology

Subject

Toxicology

Link

https://www.jstage.jst.go.jp/article/jts/40/2/40_163/_pdf

Reference19 articles.

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2. Cronin, M.T.D. and Worth, A.P. (2008): (Q)SARs for predicting effects relating to reproductive toxicity. QSAR Comb. Sci., 27, 91-100.

3. Hartung, T. and Rovida, C. (2009): Chemical regulators have overreached. Nature, 460, 1080-1081.

4. Hirota, M., Kouzuki, H., Ashikaga, T., Sono, S., Tsujita, K., Sasa, H. and Aiba, S. (2013): Artificial neural network analysis of data from multiple in vitro assays for prediction of skin sensitization potency of chemicals. Toxicol. In Vitro, 27, 1233-1246.

5. Jagt, K., Munn, S., Tørsløv, J. and Bruijn, J. (2004): Alternative approaches can reduce the use of test animals under REACH. Report EUR 21405.

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