Abstract

In biomedical research and clinical practices, Graph Neural Networks (GNNs) are playing an increasingly important role and have been applied to the problems of disease pathway detection, gene–disease relation prediction, etc. They show great potential for biomedical predictions; however, there are interpretability issues when used on complex datasets like gene expression data. Current explainability methods such as GNNExplainer are designed to explain individual instances, not the whole network. The absence of transparency hinders trust and limits the clinical/biomedical implementation of GNNs. Additionally, more interpretable models like GNN-SubNet and XGDAG do not fulfill the expectation of a clear picture for the entire network. This research addresses the limitation of the network-wide explainability of GNNs by introducing a GNN-based BioGraphEX model that incorporates interpretable methods at two levels, instance-level and network-wide level, such as gradient-based methods and SHAP (Shapley Additive Explanations). Using the GSE25097 biomedical dataset, the model achieves an accuracy of 85% and an F1 Score of 0.82, surpassing baseline methods in both predictive performance and interpretability. These results address the limitations of existing models like GNN-SubNet and XGDAG by providing both instance-level and network-wide insights. Metrics like Explanation Fidelity (83%) further validated the robustness of the explanations.

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