ArticleBMC bioinformatics2016
DrugQuest - a text mining workflow for drug association discovery.
Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 35 citations in OpenAlex.
- Pooled it
- Reviewing the Computational Landscape of Drug Repurposing: Evolution from Structure-Based Methods to LLM-Based Methods.Biomolecules · 2026Review
- Flurbiprofen@ZIF-8 nanoformulation with potential for analgesia.BMC anesthesiology · 2025Article
- Darling (v2.0): Mining disease-related databases for the detection of biomedical entity associations.Computational and structural biotechnology journal · 2025Article
- BioTextQuest v2.0: An evolved tool for biomedical literature mining and concept discovery.Computational and structural biotechnology journal · 2024Article
- Cluster-based text mining for extracting drug candidates for the prevention of COVID-19 from the biomedical literature.Journal of Taibah University Medical Sciences · 2023Article
- Prediction and Ranking of Biomarkers UsingInternational journal of molecular sciences · 2022Article
- Darling: A Web Application for Detecting Disease-Related Biomedical Entity Associations with Literature Mining.Biomolecules · 2022Article
- Review
- A Hybrid Protocol for Finding Novel Gene Targets for Various Diseases Using Microarray Expression Data Analysis and Text Mining.Methods in molecular biology (Clifton, N.J.) · 2022Article
- A review of computational drug repositioning: strategies, approaches, opportunities, challenges, and directions.Journal of cheminformatics · 2020Review
- Drug Abuse Research Trend Investigation with Text Mining.Computational and mathematical methods in medicine · 2020Article
- Text-mining in cancer research may help identify effective treatments.Translational lung cancer research · 2019Article
- Exploring the new horizons of drug repurposing: A vital tool for turning hard work into smart work.European journal of medicinal chemistry · 2019Review
- The assessment of efficient representation of drug features using deep learning for drug repositioning.BMC bioinformatics · 2019Article
- Drug repurposing in oncology: Compounds, pathways, phenotypes and computational approaches for colorectal cancer.Biochimica et biophysica acta. Reviews on cancer · 2019Review
- Adopting Text Mining on Rehabilitation Therapy Repositioning for Stroke.Frontiers in neuroinformatics · 2019Article
- Discovering major opioid-related research themes over time: A text mining technique.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2019Article
- Annotation and detection of drug effects in text for pharmacovigilance.Journal of cheminformatics · 2018Article
- Review of Drug Repositioning Approaches and Resources.International journal of biological sciences · 2018Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundText mining and data integration methods are gaining ground in the field of health sciences due to the exponential growth of bio-medical literature and information stored in biological databases. While such methods mostly try to extract bioentity associations from PubMed, very few of them are dedicated in mining other types of repositories such as chemical databases.
resultsHerein, we apply a text mining approach on the DrugBank database in order to explore drug associations based on the DrugBank "Description", "Indication", "Pharmacodynamics" and "Mechanism of Action" text fields. We apply Name Entity Recognition (NER) techniques on these fields to identify chemicals, proteins, genes, pathways, diseases, and we utilize the TextQuest algorithm to find additional biologically significant words. Using a plethora of similarity and partitional clustering techniques, we group the DrugBank records based on their common terms and investigate possible scenarios why these records are clustered together. Different views such as clustered chemicals based on their textual information, tag clouds consisting of Significant Terms along with the terms that were used for clustering are delivered to the user through a user-friendly web interface.
conclusionsDrugQuest is a text mining tool for knowledge discovery: it is designed to cluster DrugBank records based on text attributes in order to find new associations between drugs. The service is freely available at http://bioinformatics.med.uoc.gr/drugquest .
Indexed as
Identifiers
What OpenQuestion holds
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.