ArticleNAR genomics and bioinformatics2024
Predicting gene disease associations with knowledge graph embeddings for diseases with curtailed information.
Article in NAR genomics and bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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
7 citing papers in PubMed.
- A multimodal vision knowledge graph of cardiovascular disease.Nature cardiovascular research · 2026Article
- Application of knowledge graphs in rare disease research.Frontiers in public health · 2026Review
- Structural and Functional Impacts of SARS-CoV-2 Spike Protein Mutations: Insights From Predictive Modeling and Analytics.JMIR bioinformatics and biotechnology · 2025Article
- Article
- KG2ML: integrating knowledge graphs and positive unlabeled learning for identifying disease-associated genes.Frontiers in bioinformatics · 2025Article
- Cross-modal embedding integrator for disease-gene/protein association prediction using a multi-head attention mechanism.Pharmacology research & perspectives · 2024Article
- LIMO-GCN: a linear model-integrated graph convolutional network for predicting Alzheimer disease genes.Briefings in bioinformatics · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Knowledge graph embeddings (KGE) are a powerful technique used in the biomedical domain to represent biological knowledge in a low dimensional space. However, a deep understanding of these methods is still missing, and, in particular, regarding their applications to prioritize genes associated with complex diseases with reduced genetic information. In this contribution, we built a knowledge graph (KG) by integrating heterogeneous biomedical data and generated KGE by implementing state-of-the-art methods, and two novel algorithms: Dlemb and BioKG2vec. Extensive testing of the embeddings with unsupervised clustering and supervised methods showed that KGE can be successfully implemented to predict genes associated with diseases and that our novel approaches outperform most existing algorithms in both scenarios. Our findings underscore the significance of data quality, preprocessing, and integration in achieving accurate predictions. Additionally, we applied KGE to predict genes linked to Intervertebral Disc Degeneration (IDD) and illustrated that functions pertinent to the disease are enriched within the prioritized gene set.
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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.