PDA-PRGCN: identification of Piwi-interacting RNA-disease associations through subgraph projection and residual scaling-based feature augmentation

Abstract

Background Emerging evidences show that Piwi-interacting RNAs (piRNAs) play a pivotal role in numerous complex human diseases. Identifying potential piRNA-disease associations (PDAs) is crucial for understanding disease pathogenesis at molecular level. Compared to the biological wet experiments, the computational methods provide a cost-effective strategy. However, few computational methods have been developed so far. Results Here, we proposed an end-to-end model, referred to as PDA-PRGCN (PDA prediction using subgraph Projection and Residual scaling-based feature augmentation through Graph Convolutional Network). Specifically, starting with the known piRNA-disease associations represented as a graph, we applied subgraph projection to construct piRNA-piRNA and disease-disease subgraphs for the first time, followed by a residual scaling-based feature augmentation algorithm for node initial representation. Then, we adopted graph convolutional network (GCN) to learn and identify potential PDAs as a link prediction task on the constructed heterogeneous graph. Comprehensive experiments, including the performance comparison of individual components in PDA-PRGCN, indicated the significant improvement of integrating subgraph projection, node feature augmentation and dual-loss mechanism into GCN for PDA prediction. Compared with state-of-the-art approaches, PDA-PRGCN gave more accurate and robust predictions. Finally, the case studies further corroborated that PDA-PRGCN can reliably detect PDAs. Conclusion PDA-PRGCN provides a powerful method for PDA prediction, which can also serve as a screening tool for studies of complex diseases.

Databases and computational methods for the identification of piRNA-related molecules: A survey

“…Zhang et al [159] proposed a computational method called PDA-PRGCN, which uses a GCN, subgraph projection, feature augmentation, and dual-loss mechanism strategies to transform piRNA–disease association prediction into a graph link prediction task. They constructed a heterogeneous graph consisting of piRNA–piRNA subgraphs, disease–disease subgraphs, known piRNA–disease subgraphs, and learned node embeddings using GCN layers.…”

scMGCN: A Multi-View Graph Convolutional Network for Cell Type Identification in scRNA-seq Data

“…GNNs can learn the features and relationships of cells and improve the performance of different tasks. Graph convolutional networks (GCNs) are GNNs applied to single cells and diseases [36][37][38][39][40]. GNNs have also been used to analyze scRNA-seq data, such as imputation and clustering [41][42][43].…”