ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2017
Mechanism-based Pharmacovigilance over the Life Sciences Linked Open Data Cloud.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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
4 citing papers in PubMed.
- From Pharmacovigilance Signals to Mechanistic Phenotypes: Integrating ADMET, PK/PD, and Network Context to Interpret Antiviral Safety in Pregnancy.Pharmaceuticals (Basel, Switzerland) · 2026Article
- OpenPVSignal Knowledge Graph: Pharmacovigilance Signal Reports in a Computationally Exploitable FAIR Representation.Drug safety · 2025Article
- 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
- 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
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Adverse drug reactions (ADR) result in significant morbidity and mortality in patients, and a substantial proportion of these ADRs are caused by drug-drug interactions (DDIs). Pharmacovigilance methods are used to detect unanticipated DDIs and ADRs by mining Spontaneous Reporting Systems, such as the US FDA Adverse Event Reporting System (FAERS). However, these methods do not provide mechanistic explanations for the discovered drug-ADR associations in a systematic manner. In this paper, we present a systems pharmacology-based approach to perform mechanism-based pharmacovigilance. We integrate data and knowledge from four different sources using Semantic Web Technologies and Linked Data principles to generate a systems network. We present a network-based Apriori algorithm for association mining in FAERS reports. We evaluate our method against existing pharmacovigilance methods for three different validation sets. Our method has AUROC statistics of 0.7-0.8, similar to current methods, and event-specific thresholds generate AUROC statistics greater than 0.75 for certain ADRs. Finally, we discuss the benefits of using Semantic Web technologies to attain the objectives for mechanism-based pharmacovigilance.
Indexed as
Identifiers
29854169PMC5977627What 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.