🤖 AI Summary
Traditional approaches to disease–gene association prediction rely heavily on manual literature curation, which is labor-intensive and poorly scalable. To address this limitation, this work proposes a graph neural network framework operating on a heterogeneous biological graph that integrates ProtT5 protein sequence embeddings with BioBERT disease text embeddings for the first time. The method employs a multi-relational graph learning architecture within an encoder–decoder paradigm to predict novel associations. Benchmark evaluations demonstrate that the proposed model significantly outperforms 14 state-of-the-art methods. Moreover, high-confidence predictions generated by the model are corroborated by existing literature and exhibit clear biological relevance, thereby offering a powerful tool for identifying candidate disease-causing genes.
📝 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 methods. Literature-supported case studies confirm the biological relevance of high-confidence novel predictions, highlighting GLaDiGAtor's potential to discover candidate disease genes. These results underscore the power of graph convolutional networks in biomedical informatics and may ultimately facilitate drug discovery by revealing new gene-disease links. The source code and processed datasets are publicly available at https://github.com/HUBioDataLab/GLaDiGAtor.