A gentle introduction to understanding preclinical data for cancer pharmaco-omic modeling. Issue 6 (6th August 2021)

• Record Type: Journal Article
• Authors: Piyawajanusorn, Chayanit; Nguyen, Linh C; Ghislat, Ghita; Ballester, Pedro J
• Is Part Of: Briefings in bioinformatics. Volume 22:Issue 6(2021)
• Publication Date: 2021-08-06
• Subjects: pharmacogenomic modeling -- precision oncology -- phenotypic screening -- molecular profiling -- machine learningdrug response; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbab312
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: A central goal of precision oncology is to administer an optimal drug treatment to each cancer patient. A common preclinical approach to tackle this problem has been to characterize the tumors of patients at the molecular and drug response levels, and employ the resulting datasets for predictive in silico modeling (mostly using machine learning). Understanding how and why the different variants of these datasets are generated is an important component of this process. This review focuses on providing such introduction aimed at scientists with little previous exposure to this research area.