GraphPINE: Graph Importance Propagation for Interpretable Drug Response Prediction

ArXi:2504.05454v2 Announce Type: replace-cross Explainability is necessary for many tasks in biomedical research. Recent explainability methods have focused on attention, gradient, and Shapley value. These do not handle data with strong associated prior knowledge and fail to constrain explainability results based on known relationships between predictive features. We propose GraphPINE, a graph neural network (GNN) architecture leveraging domain-specific prior knowledge to initialize node importance optimized during.