Prediction of circRNA-Disease Associations Based on Graph Isomorphism Networks and Graph Sampling Aggregation.

The study of the relationship between circular RNA (circRNA) and disease is crucial for understanding the mechanisms underlying disease onset. However, relying on biological experiments to explore all potential connections between circRNAs and diseases is both time-consuming and labor-intensive. While various prediction methods have been proposed, they still possess certain limitations in their ability to extract deep features. In this study, we introduce an innovative computational framework called Graph Isomorphism Networks and Graph Sampling Aggregation for predicting unknown circRNA-disease associations (GINSACDA). Specifically, GINSACDA first computes the Gaussian interactive profile kernel (GIP) similarity and functional similarity of circRNAs, as well as the GIP similarity and semantic similarity of diseases, serving as global features. Then, node labels extracted from seven-hop subgraphs connected to the target nodes are used as local features, which are fused with the global features. Next, the fused features are input into a Graph Isomorphism Network (GIN) for feature extraction and combined with the Graph Sampling Aggregation (GraphSAGE) method to extract deeper hidden features. Finally, we employed a fully connected layer to compute the prediction scores. The results of five-fold cross-validation conducted on two datasets indicate that GINSACDA outperforms five other state-of-the-art models. Additionally, we conducted case studies on hepatocellular carcinoma and breast cancer to further validate the superior predictive capabilities of our model.