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.

Automated summary

The study proposes a novel model, PathGNN, that constructs a Graph Neural Networks (GNNs) model to capture the topological features of pathways. It achieves promising predictive performance in predicting long-term survival of cancer, and the adoption of an interpretation algorithm enables the identification of plausible pathways associated with survival.