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Breaking the paradigm: Dr Insight empowers signature-free, enhanced drug repurposing
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 effecti...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6691331/ https://www.ncbi.nlm.nih.gov/pubmed/30624606 http://dx.doi.org/10.1093/bioinformatics/btz006 |
Sumario: | 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. AVAILABILITY AND IMPLEMENTATION: Dr Insight R package is available at https://cran.r-project.org/web/packages/DrInsight/index.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |
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