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Prediction of the effect of formulation on the toxicity of chemicals.


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.

SUBMITTER: Mistry P 

PROVIDER: S-EPMC5310521 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

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Prediction of the effect of formulation on the toxicity of chemicals.

Mistry Pritesh P   Neagu Daniel D   Sanchez-Ruiz Antonio A   Trundle Paul R PR   Vessey Jonathan D JD   Gosling John Paul JP  

Toxicology research 20161031 1


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. ...[more]

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