| Literature DB >> 34260682 |
Danielle Maeser1, Robert F Gruener2, Rong Stephanie Huang3.
Abstract
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.Entities:
Keywords: biomarker identification; drug discovery; drug response prediction
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Year: 2021 PMID: 34260682 PMCID: PMC8574972 DOI: 10.1093/bib/bbab260
Source DB: PubMed Journal: Brief Bioinform ISSN: 1467-5463 Impact factor: 13.994