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

Journal: Toxicology research
Published Date: Oct 31, 2016

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

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

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