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

文献类型： 外文期刊

第一作者： Zhang, Ping

作者： Zhang, Ping;Sun, Weicheng;Wei, Dengguo;Li, Guodong;Xu, Jinsheng;Li, Li;Wei, Dengguo;Wei, Dengguo;Li, Li;You, Zhuhong;Zhao, Bowei

作者机构：

关键词： Piwi-interacting RNA; Disease; Subgraph projection; Feature augmentation; Graph convolutional network; piRNA-disease associations

期刊名称：BMC BIOINFORMATICS （ 影响因子：3.0； 五年影响因子：4.3 ）

ISSN： 1471-2105

年卷期： 2023 年 24 卷 1 期

页码：

收录情况： SCI

摘要： BackgroundEmerging 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.ResultsHere, 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.ConclusionPDA-PRGCN provides a powerful method for PDA prediction, which can also serve as a screening tool for studies of complex diseases.

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