GNN explanation methods applied to PPI networks reveal a topological signature of disease hubs with peak attribution in the 1-hop neighborhood, and a consensus framework combining shell-based scores and rankings improves prioritization of canonical cancer genes.
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Graph neural network explanations reveal a topological signature of disease-associated hubs in biological networks
GNN explanation methods applied to PPI networks reveal a topological signature of disease hubs with peak attribution in the 1-hop neighborhood, and a consensus framework combining shell-based scores and rankings improves prioritization of canonical cancer genes.