Graph transformer with disease subgraph positional encoding for improved comorbidity prediction.

Comorbidity, the co-occurrence of multiple medical conditions in a single patient, profoundly impacts disease management and outcomes. Understanding these complex interconnections is crucial, especially in contexts where comorbidities exacerbate outcomes. Leveraging insights from the human interactome and advancements in graph-based methodologies, this study introduces transformer with subgraph positional encoding (TSPE) for disease comorbidity prediction. Inspired by biologically supervised embedding, TSPE employs transformer's attention mechanisms and subgraph positional encoding (SPE) to capture interactions between nodes and disease associations. Our proposed SPE proves more effective than Laplacian positional encoding, as used in Dwivedi et al.'s graph transformer, underscoring the importance of integrating clustering and disease-specific information for improved predictive accuracy. Evaluated on real clinical benchmark datasets (RR0 and RR1), TSPE demonstrates substantial performance enhancements over the state-of-the-art method, achieving up to 28.24% higher ROC AUC (receiver operating characteristic-area under the curve) and 4.93% higher accuracy. This method shows promise for adaptation to other complex graph-based tasks and applications. The source code is available at GitHub website (xihan-qin/TSPE-GraphTransformer).