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...

Full description

Saved in:
Bibliographic Details
Published inBriefings in bioinformatics Vol. 22; no. 6
Main Authors Maeser, Danielle, Gruener, Robert F, Huang, Rong Stephanie
Format Journal Article
LanguageEnglish
Published England Oxford University Press 05.11.2021
Subjects
Antineoplastic Agents - therapeutic use
Biomarkers, Tumor - metabolism
Cell Line, Tumor
Humans
Problem Solving Protocol
Software
biomarker identification
drug discovery
drug response prediction
Online AccessGet full text

Cover

More Information
Summary: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.
Bibliography: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