ArticleJournal of chemical information and modeling2014
Pathway analysis for drug repositioning based on public database mining.
Article in Journal of chemical information and modeling, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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.
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.
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Who cites it
16 citing papers in PubMed, 51 citations in OpenAlex.
- Screening of the Pandemic Response Box Library Identified CRM1/XPO1 as an Anti-Mammarenavirus Druggable Target.Viruses · 2026Article
- Enhanced drug-disease association prediction through representation learning on similarity networks.Biology methods & protocols · 2026Article
- Improving computational drug repositioning through multi-source disease similarity networks.Scientific reports · 2025Article
- Improving the treatment of bacterial infections caused by multidrug-resistant bacteria through drug repositioning.Frontiers in pharmacology · 2024Review
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- Expression correlation attenuates within and between key signaling pathways in chronic kidney disease.BMC medical genomics · 2020Article
- Turning genome-wide association study findings into opportunities for drug repositioning.Computational and structural biotechnology journal · 2020Review
- Systematic analyses of drugs and disease indications in RepurposeDB reveal pharmacological, biological and epidemiological factors influencing drug repositioning.Briefings in bioinformatics · 2018Article
- Prediction of Protein-compound Binding Energies from Known Activity Data: Docking-score-based Method and its Applications.Molecular informatics · 2018Article
- DDR: efficient computational method to predict drug-target interactions using graph mining and machine learning approaches.Bioinformatics (Oxford, England) · 2018Article
- Quantitative Structure-activity Relationship (QSAR) Models for Docking Score Correction.Molecular informatics · 2017Article
- Cogena, a novel tool for co-expressed gene-set enrichment analysis, applied to drug repositioning and drug mode of action discovery.BMC genomics · 2016Article
- DT-Web: a web-based application for drug-target interaction and drug combination prediction through domain-tuned network-based inference.BMC systems biology · 2015Article
- Systems level analysis and identification of pathways and networks associated with liver fibrosis.PloS one · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Sixteen FDA-approved drugs were investigated to elucidate their mechanisms of action (MOAs) and clinical functions by pathway analysis based on retrieved drug targets interacting with or affected by the investigated drugs. Protein and gene targets and associated pathways were obtained by data-mining of public databases including the MMDB, PubChem BioAssay, GEO DataSets, and the BioSystems databases. Entrez E-Utilities were applied, and in-house Ruby scripts were developed for data retrieval and pathway analysis to identify and evaluate relevant pathways common to the retrieved drug targets. Pathways pertinent to clinical uses or MOAs were obtained for most drugs. Interestingly, some drugs identified pathways responsible for other diseases than their current therapeutic uses, and these pathways were verified retrospectively by in vitro tests, in vivo tests, or clinical trials. The pathway enrichment analysis based on drug target information from public databases could provide a novel approach for elucidating drug MOAs and repositioning, therefore benefiting the discovery of new therapeutic treatments for diseases.
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Registered trials
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.