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[2602.18769] GLaDiGAtor: Language-Model-Augmented Multi-Relation Graph Learning for Predicting Disease-Gene Associations
Summary
GLaDiGAtor is a novel graph neural network framework that enhances disease-gene association predictions by integrating language models and heterogeneous biological graphs, outperforming existing methods in accuracy.
Why It Matters
This research addresses the challenges of traditional methods in predicting disease-gene associations, which are critical for advancing biomedical research and drug discovery. By leveraging advanced machine learning techniques, GLaDiGAtor could significantly improve the identification of potential disease genes, thereby facilitating better diagnostics and therapeutics.
Key Takeaways
- GLaDiGAtor integrates language models with graph neural networks for improved predictions.
- The framework constructs a heterogeneous biological graph to model complex relationships.
- It outperforms 14 existing methods in predictive accuracy and generalization.
- Case studies validate the biological relevance of its predictions.
- The model's advancements could enhance drug discovery processes.
Computer Science > Machine Learning arXiv:2602.18769 (cs) [Submitted on 21 Feb 2026] Title:GLaDiGAtor: Language-Model-Augmented Multi-Relation Graph Learning for Predicting Disease-Gene Associations Authors:Osman Onur Kuzucu, Tunca Doğan View a PDF of the paper titled GLaDiGAtor: Language-Model-Augmented Multi-Relation Graph Learning for Predicting Disease-Gene Associations, by Osman Onur Kuzucu and Tunca Do\u{g}an View PDF HTML (experimental) Abstract:Understanding disease-gene associations is essential for unravelling disease mechanisms and advancing diagnostics and therapeutics. Traditional approaches based on manual curation and literature review are labour-intensive and not scalable, prompting the use of machine learning on large biomedical data. In particular, graph neural networks (GNNs) have shown promise for modelling complex biological relationships. To address limitations in existing models, we propose GLaDiGAtor (Graph Learning-bAsed DIsease-Gene AssociaTiOn pRediction), a novel GNN framework with an encoder-decoder architecture for disease-gene association prediction. GLaDiGAtor constructs a heterogeneous biological graph integrating gene-gene, disease-disease, and gene-disease interactions from curated databases, and enriches each node with contextual features from well-known language models (ProtT5 for protein sequences and BioBERT for disease text). In evaluations, our model achieves superior predictive accuracy and generalisation, outperforming 14 existing...
