生物标志物发现
药物发现
药品
药物反应
体内
癌细胞系
计算生物学
抗癌药
计算机科学
医学
生物信息学
药物开发
生物标志物
癌症
药理学
生物
蛋白质组学
内科学
癌细胞
生物技术
基因
生物化学
作者
Danielle Maeser,Robert F. Gruener,R. Stephanie Huang
摘要
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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