ArticleScientific reports2017
Discordancy Partitioning for Validating Potentially Inconsistent Pharmacogenomic Studies.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Vibroarthrographic Signal Spectral Features in 5-Class Knee Joint Classification.Sensors (Basel, Switzerland) · 2020Article
- Evaluating the consistency of large-scale pharmacogenomic studies.Briefings in bioinformatics · 2019Review
- Differential Sensitivity Analysis for Resistant Malignancies (DISARM) Identifies Common Candidate Therapies across Platinum-Resistant Cancers.Clinical cancer research : an official journal of the American Association for Cancer Research · 2019Article
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Authors and funding
2 authors.
Funding
Abstract
The Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) are two major studies that can be used to mine for therapeutic biomarkers for cancers of a large variety. Model validation using the two datasets however has proved challenging. Both predictions and signatures do not consistently validate well for models built on one dataset and tested on the other. While the genomic profiling seems consistent, the drug response data is not. Some efforts at harmonizing experimental designs has helped but not entirely removed model validation difficulties. In this paper, we present a partitioning strategy based on a data sharing concept which directly acknowledges a potential lack of concordance between datasets and in doing so, also allows for extraction of reproducible novel gene-drug interaction signatures as well as accurate test set predictions. We demonstrate these properties in a re-analysis of the GDSC and CCLE datasets.
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