Mistry, P, Neagu, D, Sanchez-Ruiz, A et al. (3 more authors) (2017) Prediction of the effect of formulation on the toxicity of chemicals. Toxicology Research, 6 (1). pp. 42-53. ISSN 2045-4538
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.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © The Royal Society of Chemistry 2017. This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 25 Oct 2016 12:30 |
Last Modified: | 05 Oct 2017 16:35 |
Published Version: | https://doi.org/10.1039/C6TX00303F |
Status: | Published |
Publisher: | Royal Society of Chemistry |
Identification Number: | 10.1039/C6TX00303F |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:106484 |