GraphAge
DNA methylation is crucial for predicting epigenetic age, but many models overlook key CpG site relationships. We introduce GraphAge, a model that represents methylation data as a graph, with nodes for CpG sites and edges for relationships like co-methylation. Using a Graph Neural Network (GNN), GraphAge predicts age by capturing both structural and positional information. Despite limited computational resources, GraphAge achieved a Mean Absolute Error (MAE) of 3.207 and a Mean Squared Error (MSE) of 25.277, slightly outperforming the state of the art. Crucially, GraphAge excels in interpretation, using a GNN explainer to uncover key CpG sites and aging pathways, revealing complex interactions and patterns that were previously inaccessible. This makes GraphAge a powerful tool not just for prediction, but for deepening our understanding of the biological mechanisms of aging, setting a new benchmark for multimodal models in this field.