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. Here, we unite and update these methodologies into one R package (oncoPredict) to facilitate the development and adoption of these tools.

⚡ This is an original paraphrased summary — not copied from the abstract. Full paper available at the source link below.

• 1 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.
• 2 Here, we unite and update these methodologies into one R package (oncoPredict) to facilitate the development and adoption of these tools.
• 3 This new OncoPredict R package can be applied to various in vitro and in vivo contexts for drug and biomarker discovery.

Understanding this could lead to better treatments, improved diagnostics, or a deeper grasp of how the human body works — benefiting patient care globally.

This summary is based on publicly available metadata and abstract. For the full research paper, visit the original source: