ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2025
Predicting Natural Product-Drug Interactions with Knowledge Graph Embeddings.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Assessing Multimodal AI for Visual Information Extraction of Pharmacology.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Natural product-drug interactions (NPDIs) occurring due to concomitant exposure to botanical products and prescription drug therapies could lead to adverse events or reduced treatment efficacy. To better understand and address potential safety concerns, researchers investigate the underlying NPDI mechanisms using in vitro and clinical studies. Given that natural products are complex mixtures of compounds that are often not well characterized, it is important to advance computational methods for novel NPDI research. Biomedical knowledge graphs (KGs) can aid in identifying potential mechanisms to support such research efforts. We evaluated the ability of several KG embedding methods to improve NPDI prediction on NP-KG, a large-scale, heterogeneous, biomedical KG. We found that the ComplEx model outperformed other KG embedding approaches in both intrinsic and extrinsic evaluations. Future work will focus on utilizing the embeddings to identify underlying mechanisms of novel, potential NPDIs.
Identifiers
40502231PMC12150722What 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.