Publication | Open Access
oncoPredict: an R package for predicting <i>in vivo</i> or cancer patient drug response and biomarkers from cell line screening data
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Citations
15
References
2021
Year
Cell Line DrugEngineeringDrug TargetR PackageOncologyBiostatisticsMolecular DiagnosticsCancer ResearchDrug ResponseCell LineOmicsPharmacologyCell BiologyBioinformaticsTarget PredictionPrognostic BiomarkersComputational BiologyRational Drug DesignCancer GenomicsSystems BiologyMedicineDrug DiscoveryHigh-throughput Screening
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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