• 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. (2017)
      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.