oncoPredict: an R package for predicting in vivo or cancer patient drug response and biomarkers from cell line screening data
Cell line drug screening datasets can be utilized for a range of different drug discovery applications from drug biomarker discovery to building translational models of drug response. Previously, we described three separate methodologies to (1) correct for general levels of drug sensitivity to enabl...
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| Vydané v: | Briefings in bioinformatics Ročník 22; číslo 6 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
England
Oxford University Press
05.11.2021
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| Predmet: | |
| ISSN: | 1467-5463, 1477-4054, 1477-4054 |
| On-line prístup: | Získať plný text |
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| Shrnutí: | Cell line drug screening datasets can be utilized for a range of different drug discovery applications from drug biomarker discovery to building translational models of drug response. Previously, we described three separate methodologies to (1) correct for general levels of drug sensitivity to enable drug-specific biomarker discovery, (2) predict clinical drug response in patients and (3) associate these predictions with clinical features to perform in vivo drug biomarker discovery. Here, we unite and update these methodologies into one R package (oncoPredict) to facilitate the development and adoption of these tools. This new OncoPredict R package can be applied to various in vitro and in vivo contexts for drug and biomarker discovery. |
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| Bibliografia: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Danielle Maeser and Robert F. Gruener authors contributed equally to this work. |
| ISSN: | 1467-5463 1477-4054 1477-4054 |
| DOI: | 10.1093/bib/bbab260 |