制剂对化学品毒性影响的预测。
Prediction of the effect of formulation on the toxicity of chemicals.
作者信息
Mistry Pritesh, Neagu Daniel, Sanchez-Ruiz Antonio, Trundle Paul R, Vessey Jonathan D, Gosling John Paul
机构信息
Artificial Intelligence Research Group , Faculty of Engineering and Informatics , University of Bradford , Bradford , UK.
Lhasa Limited , Granary Wharf House , 2 Canal Wharf , Holbeck , Leeds , LS11 9PS , UK . Email:
出版信息
Toxicol Res (Camb). 2017 Jan 1;6(1):42-53. doi: 10.1039/c6tx00303f. Epub 2016 Oct 31.
Two approaches for the prediction of which of two vehicles will result in lower toxicity for anticancer agents are presented. Machine-learning models are developed using decision tree, random forest and partial least squares methodologies and statistical evidence is presented to demonstrate that they represent valid models. Separately, a clustering method is presented that allows the ordering of vehicles by the toxicity they show for chemically-related compounds.
本文介绍了两种预测两种载体中哪一种对抗癌药物毒性更低的方法。使用决策树、随机森林和偏最小二乘法开发了机器学习模型，并提供了统计证据以证明它们是有效的模型。另外，还提出了一种聚类方法，该方法可以根据载体对化学相关化合物的毒性对其进行排序。
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