ArticlePacific Symposium on Biocomputing. Pacific Symposium on Biocomputing2020
A Literature-Based Knowledge Graph Embedding Method for Identifying Drug Repurposing Opportunities in Rare Diseases.
Article in Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 1 of them a synthesis that pooled it.
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
48 citing papers in PubMed, 1 synthesis or guideline pooled it, 74 citations in OpenAlex.
- Literature-Wide Association Studies (LWAS) for a Rare Disease: Drug Repurposing for Inflammatory Breast Cancer.Molecules (Basel, Switzerland) · 2020Pooled it
- Synonym Augmentation for Rare Disease Identification in Unstructured Data.medRxiv : the preprint server for health sciences · 2026Article
- Artificial intelligence in drug research and development: a review of methods and applications in drug repurposing.Briefings in bioinformatics · 2026Review
- Computational proteomics to enhance personalized treatment of COVID-19 and Long COVID.Clinical proteomics · 2026Review
- CellAwareGNN: Single-Cell Enhanced Knowledge Graph Foundation Model for Drug Indication Prediction.bioRxiv : the preprint server for biology · 2026Article
- Knowledge graph representation of the mappings between seizure semiology and epileptogenic zones.Scientific reports · 2026Article
- Graph-Based Gene Embedding.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Application of knowledge graphs in rare disease research.Frontiers in public health · 2026Review
- AΙ-Driven Drug Repurposing: Applications and Challenges.Medicines (Basel, Switzerland) · 2025Review
- Article
- DrugReX: an explainable drug repurposing system powered by large language models and literature-based knowledge graph.Research square · 2025Article
- Predicting Natural Product-Drug Interactions with Knowledge Graph Embeddings.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- HeteroKGRep: Heterogeneous Knowledge Graph based Drug Repositioning.Knowledge-based systems · 2024Article
- Graph Artificial Intelligence in Medicine.Annual review of biomedical data science · 2024Review
- Elucidating the semantics-topology trade-off for knowledge inference-based pharmacological discovery.Journal of biomedical semantics · 2024Article
- MSI-DTI: predicting drug-target interaction based on multi-source information and multi-head self-attention.Briefings in bioinformatics · 2024Article
- The use of artificial intelligence in the treatment of rare diseases: A scoping review.Intractable & rare diseases research · 2024Article
- Artificial intelligence in drug repurposing for rare diseases: a mini-review.Frontiers in medicine · 2024Review
- DrugRep-HeSiaGraph: when heterogenous siamese neural network meets knowledge graphs for drug repurposing.BMC bioinformatics · 2023Article
- Large-Scale Biomedical Relation Extraction Across Diverse Relation Types: Model Development and Usability Study on COVID-19.Journal of medical Internet research · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
Millions of Americans are affected by rare diseases, many of which have poor survival rates. However, the small market size of individual rare diseases, combined with the time and capital requirements of pharmaceutical R&D, have hindered the development of new drugs for these cases. A promising alternative is drug repurposing, whereby existing FDA-approved drugs might be used to treat diseases different from their original indications. In order to generate drug repurposing hypotheses in a systematic and comprehensive fashion, it is essential to integrate information from across the literature of pharmacology, genetics, and pathology. To this end, we leverage a newly developed knowledge graph, the Global Network of Biomedical Relationships (GNBR). GNBR is a large, heterogeneous knowledge graph comprising drug, disease, and gene (or protein) entities linked by a small set of semantic themes derived from the abstracts of biomedical literature. We apply a knowledge graph embedding method that explicitly models the uncertainty associated with literature-derived relationships and uses link prediction to generate drug repurposing hypotheses. This approach achieves high performance on a gold-standard test set of known drug indications (AUROC = 0.89) and is capable of generating novel repurposing hypotheses, which we independently validate using external literature sources and protein interaction networks. Finally, we demonstrate the ability of our model to produce explanations of its predictions.
Indexed as
Identifiers
31797619PMC6937428W2998922495What 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.