ArticleJournal of biomedical informatics2014
Using semantic predications to uncover drug-drug interactions in clinical data.
Article in Journal of biomedical informatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
What it found
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it, 58 citations in OpenAlex.
- Computational Advances in Drug Safety: Systematic and Mapping Review of Knowledge Engineering Based Approaches.Frontiers in pharmacology · 2019Pooled it
- Insights into the Impact of Artificial Intelligence on Psoriasis Treatment Strategies: A Mini Review.Indian dermatology online journal · 2025Article
- Drug-drug interaction prediction: databases, web servers and computational models.Briefings in bioinformatics · 2023Review
- Discovering novel drug-supplement interactions using SuppKG generated from the biomedical literature.Journal of biomedical informatics · 2022Article
- Machine Learning in Causal Inference: Application in Pharmacovigilance.Drug safety · 2022Review
- Computational Literature-based Discovery for Natural Products Research: Current State and Future Prospects.Frontiers in bioinformatics · 2022Review
- A computational approach to predict multi-pathway drug-drug interactions: A case study of irinotecan, a colon cancer medication.Saudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society · 2020Article
- Machine Learning Applications in the Evaluation and Management of Psoriasis: A Systematic Review.Journal of psoriasis and psoriatic arthritis · 2020Article
- Korean clinical entity recognition from diagnosis text using BERT.BMC medical informatics and decision making · 2020Article
- Broad-coverage biomedical relation extraction with SemRep.BMC bioinformatics · 2020Article
- A Knowledge Graph of Combined Drug Therapies Using Semantic Predications From Biomedical Literature: Algorithm Development.JMIR medical informatics · 2020Article
- Applying citizen science to gene, drug and disease relationship extraction from biomedical abstracts.Bioinformatics (Oxford, England) · 2020Article
- Opportunities and Challenges of Using Big Data to Detect Drug-Drug Interaction Risk.Clinical pharmacology and therapeutics · 2019Article
- Biomedical text mining for research rigor and integrity: tasks, challenges, directions.Briefings in bioinformatics · 2018Article
- Article
- OC-2-KB: integrating crowdsourcing into an obesity and cancer knowledge base curation system.BMC medical informatics and decision making · 2018Article
- Exploiting semantic patterns over biomedical knowledge graphs for predicting treatment and causative relations.Journal of biomedical informatics · 2018Article
- Drug-drug interaction discovery and demystification using Semantic Web technologies.Journal of the American Medical Informatics Association : JAMIA · 2017Article
- MeSHDD: Literature-based drug-drug similarity for drug repositioning.Journal of the American Medical Informatics Association : JAMIA · 2017Article
- Operationalizing Semantic Medline for meeting the information needs at point of care.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
In this study we report on potential drug-drug interactions between drugs occurring in patient clinical data. Results are based on relationships in SemMedDB, a database of structured knowledge extracted from all MEDLINE citations (titles and abstracts) using SemRep. The core of our methodology is to construct two potential drug-drug interaction schemas, based on relationships extracted from SemMedDB. In the first schema, Drug1 and Drug2 interact through Drug1's effect on some gene, which in turn affects Drug2. In the second, Drug1 affects Gene1, while Drug2 affects Gene2. Gene1 and Gene2, together, then have an effect on some biological function. After checking each drug pair from the medication lists of each of 22 patients, we found 19 known and 62 unknown drug-drug interactions using both schemas. For example, our results suggest that the interaction of Lisinopril, an ACE inhibitor commonly prescribed for hypertension, and the antidepressant sertraline can potentially increase the likelihood and possibly the severity of psoriasis. We also assessed the relationships extracted by SemRep from a linguistic perspective and found that the precision of SemRep was 0.58 for 300 randomly selected sentences from MEDLINE. Our study demonstrates that the use of structured knowledge in the form of relationships from the biomedical literature can support the discovery of potential drug-drug interactions occurring in patient clinical data. Moreover, SemMedDB provides a good knowledge resource for expanding the range of drugs, genes, and biological functions considered as elements in various drug-drug interaction pathways.
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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.