Prediction of the effect of formulation on the toxicity of chemicals. Issue 1 (1st November 2016)

• Record Type: Journal Article
• Authors: Mistry, Pritesh; Neagu, Daniel; Sanchez-Ruiz, Antonio; Trundle, Paul R.; Vessey, Jonathan D.; Gosling, John Paul
• Is Part Of: Toxicology research. Volume 6:Issue 1(2017:Jan.)
• Page Start: 42
• Page End: 53
• Publication Date: 2016-11-01
• Subjects: Toxicology -- Periodicals; 615.9005
• DOI: 10.1039/c6tx00303f
• Languages: English
• ISSNs: 2045-452X

Abstract

Abstract : Two approaches for the prediction of which of two vehicles will result in lower toxicity for anticancer agents are presented. 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.