DRAMA: Discovering Disease-related circRNA-miRNA-mRNA Axes from Disease-RNA Information Network

Non-coding RNAs are gaining prominence in biology and medicine, as they play major roles in cellular homeostasis and disease. A large number of computational methods have been recently developed for the prediction of the relationship between ncRNAs and diseases, which can alleviate the time-consuming and labor-intensive exploration among biological experiments. However, such methods have mainly focused on the association between the disease and certain types of ncRNAs such as miRNA or circRNA, thereby ignoring the impact of the interactions among ncRNAs on the diseases. We hereby propose a novel approach called DRAMA for discovering disease-related circRNA-miRNA-mRNA axes from the disease-RNA information network we constructed. Our method, using graph convolutional network, learns the characteristic representation of each biological entity by propagating and aggregating local neighbor information based on the global structure of the network. And then we design a favorable measurement to infer disease-related circRNA-miRNA-mRNA axes based on the learned embeddings. To evaluate the effectiveness of DRAMA, we conduct experiments on real-world datasets. Further analysis reveals that DRAMA outperforms other state-of-the-art baselines on most of the metrics.