Prediction of the effect of formulation on the toxicity of chemicals† †Electronic supplementary information (ESI) available: KNIME workflows of the model building process. See DOI: 10.1039/c6tx00303f Click here for additional data file. Click here for additional data file
| Autor: | Mistry, Pritesh, Neagu, Daniel, Sanchez-Ruiz, Antonio, Trundle, Paul R., Vessey, Jonathan D., Gosling, John Paul |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: | |
| Zdroj: | Toxicology Research |
| ISSN: | 2045-4538 2045-452X |
| Popis: | Two approaches for the prediction of which of two vehicles will result in lower toxicity for anticancer agents are presented. 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|