Show simple item record

dc.contributor.authorMistry, Pritesh*
dc.contributor.authorNeagu, Daniel*
dc.contributor.authorSanchez-Ruiz, A.*
dc.contributor.authorTrundle, Paul R.*
dc.contributor.authorVessey, J.D.*
dc.contributor.authorGosling, J.P.*
dc.date.accessioned2016-11-01T08:30:58Z
dc.date.available2016-11-01T08:30:58Z
dc.date.issued2017
dc.identifier.citationMistry P, Neagu D, Sanchez-Ruiz A et al (2017) Prediction of the effect of formulation on the toxicity of chemicals. Toxicology Research. 6(1): 42-53.en_US
dc.identifier.urihttp://hdl.handle.net/10454/10169
dc.descriptionYesen_US
dc.description.abstractTwo 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.en_US
dc.language.isoenen_US
dc.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/)en_US
dc.subjectToxicity; Anticancer agentsen_US
dc.titlePrediction of the effect of formulation on the toxicity of chemicalsen_US
dc.status.refereedYesen_US
dc.date.Accepted2016-10-24
dc.date.application2016-10-31
dc.typeArticleen_US
dc.type.versionAccepted Manuscripten_US
dc.identifier.doihttps://doi.org/10.1039/C6TX00303F
refterms.dateFOA2018-07-26T09:35:23Z


Item file(s)

Thumbnail
Name:
mistry_et_al_tox_res_2016.pdf
Size:
1.769Mb
Format:
PDF
Description:
Keep suppressed - has older cover ...
Thumbnail
Name:
mistry_et_al_tox_res_2016.pdf
Size:
1.783Mb
Format:
PDF
Description:
To keep suppressed
Thumbnail
Name:
Toxicology_Research_Final.pdf
Size:
3.098Mb
Format:
PDF

This item appears in the following Collection(s)

Show simple item record