title: 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.
authors.affiliation: Artificial Intelligence Research Group , Faculty of Engineering and Informatics , University of Bradford , Bradford , UK.
authors.forename: Pritesh
authors.initials: P
authors.lastname: Mistry
authors.affiliation: Artificial Intelligence Research Group , Faculty of Engineering and Informatics , University of Bradford , Bradford , UK.
authors.forename: Daniel
authors.initials: D
authors.lastname: Neagu
authors.affiliation: Lhasa Limited , Granary Wharf House , 2 Canal Wharf , Holbeck , Leeds , LS11 9PS , UK . Email: jonathan.vessey@lhasalimited.org.
authors.forename: Antonio
authors.initials: A
authors.lastname: Sanchez-Ruiz
authors.affiliation: Artificial Intelligence Research Group , Faculty of Engineering and Informatics , University of Bradford , Bradford , UK.
authors.forename: Paul R
authors.initials: PR
authors.lastname: Trundle
authors.affiliation: Lhasa Limited , Granary Wharf House , 2 Canal Wharf , Holbeck , Leeds , LS11 9PS , UK . Email: jonathan.vessey@lhasalimited.org.
authors.forename: Jonathan D
authors.initials: JD
authors.lastname: Vessey
authors.affiliation: School of Mathematics , University of Leeds , Leeds , UK.
authors.forename: John Paul
authors.initials: JP
authors.lastname: Gosling