Prediction of the effect of formulation on the toxicity of chemicals

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 the...

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Autores principales: Mistry, Pritesh, Neagu, Daniel, Sanchez-Ruiz, Antonio, Trundle, Paul R., Vessey, Jonathan D., Gosling, John Paul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Royal Society of Chemistry 2016
Materias:
Chemistry
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5310521/
https://www.ncbi.nlm.nih.gov/pubmed/28261444
http://dx.doi.org/10.1039/c6tx00303f
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author Mistry, Pritesh
Neagu, Daniel
Sanchez-Ruiz, Antonio
Trundle, Paul R.
Vessey, Jonathan D.
Gosling, John Paul
author_facet Mistry, Pritesh
Neagu, Daniel
Sanchez-Ruiz, Antonio
Trundle, Paul R.
Vessey, Jonathan D.
Gosling, John Paul
author_sort Mistry, Pritesh
collection PubMed
description 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.
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spelling pubmed-53105212017-03-01 Prediction of the effect of formulation on the toxicity of chemicals Mistry, Pritesh Neagu, Daniel Sanchez-Ruiz, Antonio Trundle, Paul R. Vessey, Jonathan D. Gosling, John Paul Toxicol Res (Camb) Chemistry 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. Royal Society of Chemistry 2016-10-31 /pmc/articles/PMC5310521/ /pubmed/28261444 http://dx.doi.org/10.1039/c6tx00303f Text en This journal is © The Royal Society of Chemistry 2017 http://creativecommons.org/licenses/by/3.0/ This article is freely available. This article is licensed under a Creative Commons Attribution 3.0 Unported Licence (CC BY 3.0)
spellingShingle Chemistry
Mistry, Pritesh
Neagu, Daniel
Sanchez-Ruiz, Antonio
Trundle, Paul R.
Vessey, Jonathan D.
Gosling, John Paul
Prediction of the effect of formulation on the toxicity of chemicals
title Prediction of the effect of formulation on the toxicity of chemicals
title_full Prediction of the effect of formulation on the toxicity of chemicals
title_fullStr Prediction of the effect of formulation on the toxicity of chemicals
title_full_unstemmed Prediction of the effect of formulation on the toxicity of chemicals
title_short Prediction of the effect of formulation on the toxicity of chemicals
title_sort prediction of the effect of formulation on the toxicity of chemicals
topic Chemistry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5310521/
https://www.ncbi.nlm.nih.gov/pubmed/28261444
http://dx.doi.org/10.1039/c6tx00303f
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