Document Type
Article
Publication Date
2019
Abstract
Motivation: Transcriptome-based computational drug repurposing has attracted considerable interest by bringing about faster and more cost-effective drug discovery. Nevertheless, key limitations of the current drug connectivity-mapping paradigm have been long overlooked, including the lack of effective means to determine optimal query gene signatures. Results: The novel approach Dr Insight implements a frame-breaking statistical model for the ‘hand-shake’ between disease and drug data. The genome-wide screening of concordantly expressed genes (CEGs) eliminates the need for subjective selection of query signatures, added to eliciting better proxy for potential disease-specific drug targets. Extensive comparisons on simulated and real cancer datasets have validated the superior performance of Dr Insight over several popular drug-repurposing methods to detect known cancer drugs and drug–target interactions. A proof-of-concept trial using the TCGA breast cancer dataset demonstrates the application of Dr Insight for a comprehensive analysis, from redirection of drug therapies, to a systematic construction of disease-specific drug-target networks.
DOI
https://doi.org/10.1093/bioinformatics/btz006
Repository Citation
Chan, Jinyan; Wang, Xuan; Turner, Jacob A.; Baldwin, Nicole E.; and Gu, Jinghua, "Breaking the paradigm: Dr Insight empowers signature-free, enhanced drug repurposing" (2019). Faculty Publications. 3.
https://scholarworks.sfasu.edu/mathandstats_facultypubs/3
Supplementary Materials
Comments
Jinyan Chan, Xuan Wang, Jacob A Turner, Nicole E Baldwin, Jinghua Gu, Breaking the paradigm: Dr Insight empowers signature-free, enhanced drug repurposing, Bioinformatics, Volume 35, Issue 16, 15 August 2019, Pages 2818–2826, https://doi.org/10.1093/bioinformatics/btz006