Predicting miRNA-disease associations based on graph convolutional network with path learning

Identifying miRNA-disease associations (MDAs) is crucial for improving the diagnosis and treatment of various diseases. However, biological experiments can be time-consuming and expensive. To overcome these challenges, computational approaches have been developed, with Graph Convolutional Networks (GCNs) showing promising results in MDAs prediction. The success of GCN-based methods relies on learning a meaningful spatial operator to extract effective node feature representations. To enhance the inference of MDAs, we propose a novel method called PGCNMDA, which employs graph convolutional networks with a learning graph spatial operator from paths. This approach enables the generation of meaningful spatial convolutions from paths in GCNs, leading to improved prediction performance. We evaluate PGCNMDA using 5-fold cross-validation (5-CV) on databases HMDD v2.0 and HMDD v3.2. Additionally, we compare it with other methods. The experimental results demonstrate that PGCNMDA outperforms other miRNA-disease association prediction methods.