ArticleScientific reports2019
Drug prioritization using the semantic properties of a knowledge graph.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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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.
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Who cites it
23 citing papers in PubMed, 45 citations in OpenAlex.
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- Discovering anticancer drug target combinations via network-informed signaling-based approach.Communications medicine · 2025Article
- A Survey on Unifying Large Language Models and Knowledge Graphs for Biomedicine and Healthcare.KDD : proceedings. International Conference on Knowledge Discovery & Data Mining · 2025Article
- Cross-modal embedding integrator for disease-gene/protein association prediction using a multi-head attention mechanism.Pharmacology research & perspectives · 2024Article
- Anticancer Target Combinations: Network-Informed Signaling-Based Approach to Discovery.bioRxiv : the preprint server for biology · 2024Article
- Optimizing drug combination and mechanism analysis based on risk pathway crosstalk in pan cancer.Scientific data · 2024Article
- Computational drug discovery approaches identify mebendazole as a candidate treatment for autosomal dominant polycystic kidney disease.Frontiers in pharmacology · 2024Article
- Article
- Latent disease similarities and therapeutic repurposing possibilities uncovered by multi-modal generative topic modeling of human diseases.Bioinformatics advances · 2023Article
- Towards a knowledge graph for pre-/probiotics and microbiota-gut-brain axis diseases.Scientific reports · 2022Article
- Mining on Alzheimer's diseases related knowledge graph to identity potential AD-related semantic triples for drug repurposing.BMC bioinformatics · 2022Article
- Design and application of a knowledge network for automatic prioritization of drug mechanisms.Bioinformatics (Oxford, England) · 2022Article
- A Study on Methodologies of Drug Repositioning Using Biomedical Big Data: A Focus on Diabetes Mellitus.Endocrinology and metabolism (Seoul, Korea) · 2022Article
- A semantic web technology index.Scientific reports · 2022Article
- Causal reasoning over knowledge graphs leveraging drug-perturbed and disease-specific transcriptomic signatures for drug discovery.PLoS computational biology · 2022Article
- Drug repositioning: A bibliometric analysis.Frontiers in pharmacology · 2022Review
- Medical concept normalization in clinical trials with drug and disease representation learning.Bioinformatics (Oxford, England) · 2021Article
- A data-driven methodology towards evaluating the potential of drug repurposing hypotheses.Computational and structural biotechnology journal · 2021Article
- A semantic relationship mining method among disorders, genes, and drugs from different biomedical datasets.BMC medical informatics and decision making · 2020Article
- Drug2ways: Reasoning over causal paths in biological networks for drug discovery.PLoS computational biology · 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
12 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Compounds that are candidates for drug repurposing can be ranked by leveraging knowledge available in the biomedical literature and databases. This knowledge, spread across a variety of sources, can be integrated within a knowledge graph, which thereby comprehensively describes known relationships between biomedical concepts, such as drugs, diseases, genes, etc. Our work uses the semantic information between drug and disease concepts as features, which are extracted from an existing knowledge graph that integrates 200 different biological knowledge sources. RepoDB, a standard drug repurposing database which describes drug-disease combinations that were approved or that failed in clinical trials, is used to train a random forest classifier. The 10-times repeated 10-fold cross-validation performance of the classifier achieves a mean area under the receiver operating characteristic curve (AUC) of 92.2%. We apply the classifier to prioritize 21 preclinical drug repurposing candidates that have been suggested for Autosomal Dominant Polycystic Kidney Disease (ADPKD). Mozavaptan, a vasopressin V2 receptor antagonist is predicted to be the drug most likely to be approved after a clinical trial, and belongs to the same drug class as tolvaptan, the only treatment for ADPKD that is currently approved. We conclude that semantic properties of concepts in a knowledge graph can be exploited to prioritize drug repurposing candidates for testing in clinical trials.
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What 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.