PNAGMDA: A Principal Neighborhood Aggregation Based Graph Neural Network for miRNA-disease association prediction

Increasing research suggests that microRNAs (miRNAs) serve an essential function as biomarkers in various diseases. The variations in miRNA expression can influence their corresponding mRNAs, which, in turn, regulate the expression of target genes. Recently, graph neural networks (GNNs) have been widely utilized to predict miRNA-disease associations. However, a single GNN model is insufficient for fully learning node representations. Furthermore, individual aggregation methods struggle to effectively extract diverse structural information and node weights. To address these challenges, we propose a method that incorporates Principal Neighborhood Aggregation (PNA) and Graph Attention Networks (GAT) for miRNA-disease association prediction. First, we integrated multiple datasets to construct a weighted heterogeneous graph that models miRNA-LncRNA-disease interactions. Subsequently, PNA extracted node representations using multiple aggregators simultaneously. Additionally, features derived from both PNA and GAT were fused using an attention mechanism. These combined representations were then fed into a fully connected neural network for prediction. Experimental results demonstrate that PNAGMDA achieves exceptional performance, with AUC values of 93.82% and 92.77% on HMDD v2.0 and v3.2, respectively. Case studies, along with supplementary findings, confirm PNAGMDA's reliability for miRNA-disease prediction.