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Data vignettes for the application of response surface models in drug combination analysis

This data set contains the data used in Twarog et al. (2021) to examine the robustness and utility of response surface models in drug combination analysis. It includes simulated experimental data for the evaluation of traditional index methods, as well as a processed library of interaction metrics e...

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Detalles Bibliográficos
Autores principales: Twarog, Nathaniel R., Martinez, Nancy E., Gartrell, Jessica, Xie, Jia, Tinkle, Christopher L., Shelat, Anang A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8461350/
https://www.ncbi.nlm.nih.gov/pubmed/34589567
http://dx.doi.org/10.1016/j.dib.2021.107400
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author Twarog, Nathaniel R.
Martinez, Nancy E.
Gartrell, Jessica
Xie, Jia
Tinkle, Christopher L.
Shelat, Anang A.
author_facet Twarog, Nathaniel R.
Martinez, Nancy E.
Gartrell, Jessica
Xie, Jia
Tinkle, Christopher L.
Shelat, Anang A.
author_sort Twarog, Nathaniel R.
collection PubMed
description This data set contains the data used in Twarog et al. (2021) to examine the robustness and utility of response surface models in drug combination analysis. It includes simulated experimental data for the evaluation of traditional index methods, as well as a processed library of interaction metrics evaluated on the Merck OncoPolyPharmacology Screen (O'Neil et al., 2016), the scripts used to implement those metrics on all tested combinations in that screen, and scripts to evaluate the performance of those metrics in comparison with real-world mechanistic classifications. Finally, the data set includes data from several published and unpublished drug combination experiments, and scripts which allow the analyses of those experiments to be replicated and applied to new data.
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spelling pubmed-84613502021-09-28 Data vignettes for the application of response surface models in drug combination analysis Twarog, Nathaniel R. Martinez, Nancy E. Gartrell, Jessica Xie, Jia Tinkle, Christopher L. Shelat, Anang A. Data Brief Data Article This data set contains the data used in Twarog et al. (2021) to examine the robustness and utility of response surface models in drug combination analysis. It includes simulated experimental data for the evaluation of traditional index methods, as well as a processed library of interaction metrics evaluated on the Merck OncoPolyPharmacology Screen (O'Neil et al., 2016), the scripts used to implement those metrics on all tested combinations in that screen, and scripts to evaluate the performance of those metrics in comparison with real-world mechanistic classifications. Finally, the data set includes data from several published and unpublished drug combination experiments, and scripts which allow the analyses of those experiments to be replicated and applied to new data. Elsevier 2021-09-20 /pmc/articles/PMC8461350/ /pubmed/34589567 http://dx.doi.org/10.1016/j.dib.2021.107400 Text en © 2021 The Authors. Published by Elsevier Inc. https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Data Article
Twarog, Nathaniel R.
Martinez, Nancy E.
Gartrell, Jessica
Xie, Jia
Tinkle, Christopher L.
Shelat, Anang A.
Data vignettes for the application of response surface models in drug combination analysis
title Data vignettes for the application of response surface models in drug combination analysis
title_full Data vignettes for the application of response surface models in drug combination analysis
title_fullStr Data vignettes for the application of response surface models in drug combination analysis
title_full_unstemmed Data vignettes for the application of response surface models in drug combination analysis
title_short Data vignettes for the application of response surface models in drug combination analysis
title_sort data vignettes for the application of response surface models in drug combination analysis
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8461350/
https://www.ncbi.nlm.nih.gov/pubmed/34589567
http://dx.doi.org/10.1016/j.dib.2021.107400
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