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Issue 1, 2017
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Prediction of the effect of formulation on the toxicity of chemicals

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Abstract

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.

Graphical abstract: Prediction of the effect of formulation on the toxicity of chemicals

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Publication details

The article was received on 18 Jul 2016, accepted on 24 Oct 2016 and first published on 31 Oct 2016


Article type: Paper
DOI: 10.1039/C6TX00303F
Citation: Toxicol. Res., 2017,6, 42-53
  • Open access: Creative Commons BY license
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    Prediction of the effect of formulation on the toxicity of chemicals

    P. Mistry, D. Neagu, A. Sanchez-Ruiz, P. R. Trundle, J. D. Vessey and J. P. Gosling, Toxicol. Res., 2017, 6, 42
    DOI: 10.1039/C6TX00303F

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