PDA-PAGCN: Predicting Disease-Related PiRNA Based on Proxy Attention Graph Convolutional Network

Piwi-interacting RNA (piRNA) is a key biomarker for complex disease diagnosis and prediction. Predicting piRNA-disease associations (PDA) is crucial for revealing their genetic mechanisms. In this study, a method PDA-PAGCN based on proxy attention graph convolutional network for predicting PDA. Firstly, a heterogeneous network was constructed based on the similarity and association information of piRNA and disease, which is then input into a graph convolutional network, and the feature dimensions are aligned through the group feature transformation module to obtain initial features. Subsequently, the Topk graph pooling method was employed to obtain feature subgraphs from these initial features. Finally, we fuse these feature subgraphs with the initial features using a proxy attention mechanism and calculate cosine similarity association scores to derive the final PDA reconstruction scores. The predictive performance of PDA-PAGCN is validated through five-fold cross-validation experiments, achieving an AUC of 0.9667 and an ACC of 0.9707. Case studies on two human diseases further confirm the reliability of PDA-PAGCN in practical applications. Therefore, PDA-PAGCN is proved to be effective in predicting hidden PDA.