Evidence map›Paper›PMID 30624606›Full record

ArticleBioinformatics (Oxford, England)2019

Breaking the paradigm: Dr Insight empowers signature-free, enhanced drug repurposing.

Jinyan Chan, Xuan Wang, Jacob A Turner, Nicole E Baldwin, Jinghua Gu

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

30 citing papers in PubMed.

  1. Article
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  9. Informatics on Drug Repurposing for Breast Cancer.Drug design, development and therapy · 2023
    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Jinyan ChanBaylor Scott & White Research Institute, Dallas, TX, USA.
Xuan WangBaylor Scott & White Research Institute, Dallas, TX, USA.
Jacob A TurnerDepartment of Mathematics and Statistics, Stephen F. Austin State University, Nacogdoches, TX, USA.
Nicole E BaldwinBaylor Scott & White Research Institute, Dallas, TX, USA.
Jinghua GuBaylor Scott & White Research Institute, Dallas, TX, USA.

Funding

Scientific CoreU19AI082715 · NIAID · WEILL MEDICAL COLL OF CORNELL UNIV · PI PASCUAL, MARIA VIRGINIA · 2009 to 2018
$6.4M
NIAID NIH HHS U19 AI082715
6 · The paper itself

Abstract

motivationTranscriptome-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.

resultsThe 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.

Indexed as

Drug RepositioningDrug DiscoveryModels, StatisticalSoftwareTranscriptome

Identifiers

PMID30624606
PMCPMC6691331

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.