AI Insight
GraphPINE is a graph neural network architecture designed to improve both predictive performance and interpretability in drug response prediction by incorporating domain-specific prior knowledge directly into the model's training process. Unlike conventional explainability methods such as attention mechanisms, gradient-based approaches, or Shapley values, GraphPINE initializes and updates node importance scores using a biological gene-gene graph and drug-target interaction data, allowing prior knowledge to constrain and inform feature learning rather than being applied only after prediction. Applied to cancer drug response prediction across over 5,000 genes and 952 drugs, the model achieves a PR-AUC of 0.894 and ROC-AUC of 0.796, demonstrating competitive performance alongside improved biological interpretability.
Why it matters
Improving interpretability in drug response prediction models is critical for clinical and research trust, as it allows scientists to understand which gene-drug relationships drive predictions and potentially identify new therapeutic targets or biomarkers for cancer treatment.
arXiv:2504.05454v2 Announce Type: replace-cross
Abstract: 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 training for drug response prediction. Typically, a manual post-prediction step examines literature (i.e., prior knowledge) to understand returned predictive features. While node importance can be obtained for gradient and attention after prediction, node importance from these methods lacks complementary prior knowledge; GraphPINE seeks to overcome this limitation. GraphPINE differs from other GNN gating methods by utilizing an LSTM-like sequential format. We introduce an importance propagation layer that unifies 1) updates for feature matrix and node importance and 2) uses GNN-based graph propagation of feature values. This initialization and updating mechanism allows for informed feature learning and improved graph representation.
We apply GraphPINE to cancer drug response prediction using drug screening and gene data collected for over 5,000 gene nodes included in a gene-gene graph with a drug-target interaction (DTI) graph for initial importance. The gene-gene graph and DTIs were obtained from curated sources and weighted by article count discussing relationships between drugs and genes. GraphPINE achieves a PR-AUC of 0.894 and ROC-AUC of 0.796 across 952 drugs. Code is available at https://anonymous.4open.science/r/GraphPINE-40DE.
Source: GraphPINE: Graph Importance Propagation for Interpretable Drug Response Prediction