ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2020
Predicting Polypharmacy Side-effects Using Knowledge Graph Embeddings.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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The trial behind it
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Who cites it
19 citing papers in PubMed, 46 citations in OpenAlex.
- Robust prediction of drug combination side effects in realistic settings.PLoS computational biology · 2026Article
- AMIUgraph: analysis and modeling of interactions for utility-driven benchmarking of graph-based models in healthcare.BMC medical informatics and decision making · 2026Article
- KRDQN: An Interpretable Prediction Framework for Adverse Drug Reactions via Knowledge-Graph Reinforced Deep Q-Learning.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Smart aging: integrating AI into elderly healthcare.BMC geriatrics · 2025Review
- A novel approach for target deconvolution from phenotype-based screening using knowledge graph.Scientific reports · 2025Article
- Predicting phage-host interaction via hyperbolic Poincaré graph embedding and large-scale protein language technique.iScience · 2025Article
- Fast polypharmacy side effect prediction using tensor factorization.Bioinformatics (Oxford, England) · 2024Article
- Improving Molecule Generation and Drug Discovery with a Knowledge-enhanced Generative Model.ArXiv · 2024Article
- Multi-task learning for predicting synergistic drug combinations based on auto-encoding multi-relational graphs.iScience · 2023Article
- Automatic knowledge extraction from Chinese electronic medical records and rheumatoid arthritis knowledge graph construction.Quantitative imaging in medicine and surgery · 2023Article
- DPSP: a multimodal deep learning framework for polypharmacy side effects prediction.Bioinformatics advances · 2023Article
- Polypharmacy and precision medicine.Cambridge prisms. Precision medicine · 2023Review
- Detection of polypharmacy side effects by integrating multiple data sources and convolutional neural networks.Molecular diversity · 2022Article
- Towards a knowledge graph for pre-/probiotics and microbiota-gut-brain axis diseases.Scientific reports · 2022Article
- SimVec: predicting polypharmacy side effects for new drugs.Journal of cheminformatics · 2022Article
- Computational methods, databases and tools for synthetic lethality prediction.Briefings in bioinformatics · 2022Review
- Task-driven knowledge graph filtering improves prioritizing drugs for repurposing.BMC bioinformatics · 2022Article
- The potential of a data centred approach & knowledge graph data representation in chemical safety and drug design.Computational and structural biotechnology journal · 2022Review
- Cold-Start Problems in Data-Driven Prediction of Drug-Drug Interaction Effects.Pharmaceuticals (Basel, Switzerland) · 2021Article
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 at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Polypharmacy is the use of drug combinations and is commonly used for treating complex and terminal diseases. Despite its effectiveness in many cases, it poses high risks of adverse side effects. Polypharmacy side-effects occur due to unwanted interactions of combined drugs, and they can cause severe complications to patients which results in increasing the risks of morbidity and leading to new mortalities. The use of drug polypharmacy is currently in its early stages; thus, the knowledge of their probable side-effects is limited. This encouraged multiple works to investigate machine learning techniques to efficiently and reliably predict adverse effects of drug combinations. In this context, the Decagon model is known to provide state-of-the-art results. It models polypharmacy side-effect data as a knowledge graph and formulates finding possible adverse effects as a link prediction task over the knowledge graph. The link prediction is solved using an embedding model based on graph convolutions. Despite its effectiveness, the Decagon approach still suffers from a high rate of false positives. In this work, we propose a new knowledge graph embedding technique that uses multi-part embedding vectors to predict polypharmacy side-effects. Like in the Decagon model, we model polypharmacy side effects as a knowledge graph. However, we perform the link prediction task using an approach based on tensor decomposition. Our experimental evaluation shows that our approach outperforms the Decagon model with 12% and 16% margins in terms of the area under the ROC and precision recall curves, respectively.
Identifiers
32477666PMC7233093W3030536242What 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.