Graph neural network explanations reveal a topological signature of disease-associated hubs in biological networks
arXiv:2605.21502v1 Announce Type: cross Abstract: Graph neural networks (GNNs) are increasingly used to model biological systems, yet the reliability of post-hoc explanation methods for recovering meaningful molecular mechanisms remains unclear. Here, we systematically evaluate four widely used approaches: Saliency Attribution (SA), Integrated Gradients (IG), GNNExplainer, and Layer-wise Relevance Propagation (LRP) for identifying disease-relevant structure in breast cancer RNA-seq data projected onto a protein-protein interaction network. Using synthetic benchmarks with known ground-truth mot
The increasing adoption of GNNs in biological research necessitates rigorous evaluation of their explainability to ensure reliable scientific discovery and clinical application.
Improving the explainability of GNNs in biological systems will accelerate drug discovery, disease diagnostics, and personalized medicine by providing clearer insights into molecular mechanisms.
The reliability of AI-driven insights into complex biological networks is being significantly enhanced, moving towards more trustworthy and actionable computational biology.
- · Biomedical AI researchers
- · Pharmaceutical companies
- · Personalized medicine developers
- · Patients
- · Black-box AI models in biology
- · Traditional drug discovery methods
Increased trust and adoption of explainable AI in biological and medical research.
Faster discovery of novel drug targets and diagnostic biomarkers through interpretable AI models.
Revolutionized clinical decision-making and personalized therapeutic strategies based on AI-derived mechanistic insights.
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Read at arXiv cs.LG