Risk stratification and pathway analysis based on graph neural network and interpretable algorithm

BACKGROUND: Pathway-based analysis of transcriptomic data has shown greater stability and better performance than traditional gene-based analysis. Until now, some pathway-based deep learning models have been developed for bioinformatic analysis, but these models have not fully considered the topological features of pathways, which limits the performance of the final prediction result. RESULTS: To address this issue, we propose a novel model, called PathGNN, which constructs a Graph Neural Networks (GNNs) model that can capture topological features of pathways. As a case, PathGNN was applied to predict long-term survival of four types of cancer and achieved promising predictive performance when compared to other common methods. Furthermore, the adoption of an interpretation algorithm enabled the identification of plausible pathways associated with survival. CONCLUSION: PathGNN demonstrates that GNN can be effectively applied to build a pathway-based model, resulting in promising predictive power. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-022-04950-1.