Graph neural network explanations reveal a topological signature of disease-associated hubs in biological networks
Researchers have evaluated four popular explanation methods for graph neural networks (GNNs) to understand their effectiveness in identifying disease-associated structures within biological networks. Using synthetic data and breast cancer RNA sequencing data, the study found that different methods excel at uncovering distinct types of biological signals, such as single-node drivers or distributed pathways. By combining consensus scores from multiple explainers and incorporating topological information, the researchers improved the prioritization of key cancer genes and the recovery of biologically relevant signaling pathways. AI
IMPACT Improves biological interpretability of GNNs, potentially leading to more accurate disease diagnosis and drug discovery.
