Understanding comorbidity between human diseases is essential for uncovering shared pathophysiological mechanisms and improving diagnostic and therapeutic strategies. Although prior studies have investigated genetic and network-based disease associations, they often overlook the fragmented nature of disease modules within the human interactome. To address this limitation, we introduce Fragmented Disease Subgraphs with Component-Level Attention for Comorbidity Prediction (FDS-CAP), a novel graph-based deep learning framework. FDS-CAP first embeds fragmented disease subgraphs using Subgraph Neural Networks (SUBGNN) with component-level attention, then applies a variational comorbidity predictor built upon a Variational Graph Auto-Encoder that is used to predict comorbid disease associations within the Human Disease Network. SUBGNN encodes disease subgraphs by propagating information at the connected component level across three property-aware channels-capturing positional, neighborhood, and structural roles-and integrates a component-level attention mechanism that weighs each connected component based on its significance to the overall subgraph representation. A core contribution of our method is the attention-based aggregation of connected component embeddings, enabling more accurate and expressive disease representations that reflect the biological complexity in fragmented disease subgraphs for improved comorbidity prediction. FDS-CAP achieves state-of-the-art performance for comorbidity prediction on a benchmark dataset, with an AUROC of 0.966. We further illustrate its biological interpretability through a single representative case study on glioma, showing that attention-weighted subgraph components capture meaningful patterns associated with disease mechanisms.
Altayyar et al. (Thu,) studied this question.