ArticleJournal of the American Medical Informatics Association : JAMIA2017
Drug-drug interaction discovery and demystification using Semantic Web technologies.
Article in Journal of the American Medical Informatics Association : JAMIA, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 17 papers, 2 of them syntheses that pooled it.
What it found
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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
17 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Recent Progress in Traditional Chinese Medicines and Their Mechanism in the Treatment of Allergic Rhinitis.Journal of healthcare engineering · 2022Pooled it
- Computational Advances in Drug Safety: Systematic and Mapping Review of Knowledge Engineering Based Approaches.Frontiers in pharmacology · 2019Pooled it
- Anti-DDI Resource: A Dataset for Potential Negative Reported Interaction Combinations to Improve Medical Research and Decision-Making.Journal of healthcare engineering · 2022Article
- A Rule-Based Inference Framework to Explore and Explain the Biological Related Mechanisms of Potential Drug-Drug Interactions.Computational and mathematical methods in medicine · 2022Article
- Improving bioinformatics software quality through incorporation of software engineering practices.PeerJ. Computer science · 2022Article
- A Data-Driven Medical Decision Framework for Associating Adverse Drug Events with Drug-Drug Interaction Mechanisms.Journal of healthcare engineering · 2022Article
- A novel computational drug repurposing approach for Systemic Lupus Erythematosus (SLE) treatment using Semantic Web technologies.Saudi journal of biological sciences · 2021Article
- Health informatics publication trends in Saudi Arabia: a bibliometric analysis over the last twenty-four years.Journal of the Medical Library Association : JMLA · 2021Article
- 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
- Prediction of Drug-Drug Interactions by Using Profile Fingerprint Vectors and Protein Similarities.Healthcare informatics research · 2020Article
- Evaluation of knowledge graph embedding approaches for drug-drug interaction prediction in realistic settings.BMC bioinformatics · 2019Article
- Clopidogrel Drug Interactions and Serious Bleeding: Generating Real-World Evidence via Automated High-Throughput Pharmacoepidemiologic Screening.Clinical pharmacology and therapeutics · 2019Article
- Enhancing Clinical Data and Clinical Research Data with Biomedical Ontologies - Insights from the Knowledge Representation Perspective.Yearbook of medical informatics · 2019Article
- Identifying Common Methods Used by Drug Interaction Experts for Finding Evidence About Potential Drug-Drug Interactions: Web-Based Survey.Journal of medical Internet research · 2019Article
- Enabling Web-scale data integration in biomedicine through Linked Open Data.NPJ digital medicine · 2019Review
- Data-driven prediction of adverse drug reactions induced by drug-drug interactions.BMC pharmacology & toxicology · 2017Article
- Mechanism-based Pharmacovigilance over the Life Sciences Linked Open Data Cloud.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2017Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo develop a novel pharmacovigilance inferential framework to infer mechanistic explanations for asserted drug-drug interactions (DDIs) and deduce potential DDIs. MATERIALS AND
methodsA mechanism-based DDI knowledge base was constructed by integrating knowledge from several existing sources at the pharmacokinetic, pharmacodynamic, pharmacogenetic, and multipathway interaction levels. A query-based framework was then created to utilize this integrated knowledge base in conjunction with 9 inference rules to infer mechanistic explanations for asserted DDIs and deduce potential DDIs.
resultsThe drug-drug interactions discovery and demystification (D3) system achieved an overall 85% recall rate in terms of inferring mechanistic explanations for the DDIs integrated into its knowledge base, while demonstrating a 61% precision rate in terms of the inference or lack of inference of mechanistic explanations for a balanced, randomly selected collection of interacting and noninteracting drug pairs. DISCUSSION: The successful demonstration of the D3 system's ability to confirm interactions involving well-studied drugs enhances confidence in its ability to deduce interactions involving less-studied drugs. In its demonstration, the D3 system infers putative explanations for most of its integrated DDIs. Further enhancements to this work in the future might include ranking interaction mechanisms based on likelihood of applicability, determining the likelihood of deduced DDIs, and making the framework publicly available.
conclusionThe D3 system provides an early-warning framework for augmenting knowledge of known DDIs and deducing unknown DDIs. It shows promise in suggesting interaction pathways of research and evaluation interest and aiding clinicians in evaluating and adjusting courses of drug therapy.
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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.