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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.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>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.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>PDA-PRGCN provides a powerful method for PDA prediction, which can also serve as a screening tool for studies of complex diseases.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-022-05073-3","type":"journal-article","created":{"date-parts":[[2023,1,17]],"date-time":"2023-01-17T11:14:24Z","timestamp":1673954064000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["PDA-PRGCN: identification of Piwi-interacting RNA-disease associations through subgraph projection and residual scaling-based feature augmentation"],"prefix":"10.1186","volume":"24","author":[{"given":"Ping","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Weicheng","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Dengguo","family":"Wei","sequence":"additional","affiliation":[]},{"given":"Guodong","family":"Li","sequence":"additional","affiliation":[]},{"given":"Jinsheng","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Zhuhong","family":"You","sequence":"additional","affiliation":[]},{"given":"Bowei","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Li","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,1,17]]},"reference":[{"key":"5073_CR1","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1016\/j.molcel.2007.05.021","volume":"26","author":"AG Seto","year":"2007","unstructured":"Seto AG, Kingston RE, Lau NC. 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