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Prediction of the effect of formulation on the toxicity of chemicals
Mistry, Pritesh Neagu, Daniel Sanchez-Ruiz, A. Trundle, Paul R. Vessey, J.D. Gosling, J.P.
Mistry, Pritesh
Neagu, Daniel
Sanchez-Ruiz, A.
Trundle, Paul R.
Vessey, J.D.
Gosling, J.P.
Publication Date
2017
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Rights
© 2016 The Authors. This is an Open Access article licensed under the
Creative Commons CC-BY license (http://creativecommons.org/licenses/by/3.0/)
Peer-Reviewed
Yes
Open Access status
openAccess
Accepted for publication
2016-10-24
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Department
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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.
Version
Accepted manuscript
Citation
Mistry P, Neagu D, Sanchez-Ruiz A et al (2017) Prediction of the effect of formulation on
the toxicity of chemicals. Toxicology Research. 6(1): 42-53.
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Article