Consensus of classification trees for skin sensitisation hazard prediction
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- 作者：D. Asturiol ; david.asturiol-bofill@ec.europa.eu" class="auth_mail" title="E-mail the corresponding author ; S. Casati ; A. Worth
- 关键词：QSAR ; Skin sensitisation ; In vitro ; In silico ; Prediction ; Decision tree
- 刊名：Toxicology in Vitro
- 出版年：2016
- 出版时间：October 2016
- 年：2016
- 卷：36
- 期：Complete
- 页码：197-209
- 全文大小：500 K
文摘
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A consensus of classification trees to predict skin sensitisation hazard
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Database containing LLNA, DPRA, KeratinoSens™, h-CLAT, and in silico predictions
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In silico descriptors showed the highest accuracy in predicting LLNA hazard
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The model predicts LLNA classification with an accuracy of 0.93 for 269 chemicals
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The model provides a measure of confidence associated to the prediction