Yes / 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.
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/10169 |
| Date | 31 October 2016 |
| Creators | Mistry, Pritesh, Neagu, Daniel, Sanchez-Ruiz, A., Trundle, Paul R., Vessey, J.D., Gosling, J.P. |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Article, Accepted manuscript |
| Rights | © 2016 The Authors. This is an Open Access article licensed under the Creative Commons CC-BY license (http://creativecommons.org/licenses/by/3.0/), CC-BY |
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