{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T18:16:18Z","timestamp":1787940978389,"version":"build-2784847793"},"reference-count":36,"publisher":"Oxford University Press (OUP)","issue":"8","license":[{"start":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T00:00:00Z","timestamp":1784678400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62472250"],"award-info":[{"award-number":["62472250"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62473179"],"award-info":[{"award-number":["62473179"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key R&D Program of Shandong Province, China","award":["2025CXGC010113"],"award-info":[{"award-number":["2025CXGC010113"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,8,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>MicroRNAs (miRNAs) are key post-transcriptional regulators involved in diverse biological processes, and their dysregulation is closely associated with the onset and progression of many diseases. Accurate prediction of miRNA-disease association types is therefore essential for understanding disease mechanisms and advancing precision medicine. Although computational methods provide efficient alternatives to wet-lab experiments, existing approaches often focus on binary association prediction, inadequately integrate local semantic dependencies and global topological structures, and suffer from class imbalance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>To address these limitations, we propose MRGBMDAT, a multi-relational graph encoder network with bilinear fusion for miRNA-disease association type prediction. Specifically, a multi-relational graph convolution module with bidirectional cross-attention captures global topological structures, while a local subgraph sampling module extracts local semantic dependencies. A bilinear fusion decoder with element-wise attention jointly models their linear and nonlinear interactions. In addition, an iterative feature similarity-based negative sample selection strategy is introduced to alleviate class imbalance. Experimental results on the HMDD v3.2 dataset demonstrate that MRGBMDAT significantly outperforms five state-of-the-art methods across multiple evaluation metrics, exhibiting strong discriminative power and generalization capability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The source code is publicly available at https:\/\/github.com\/CDMBlab\/MRGBMDAT.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag534","type":"journal-article","created":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T11:27:42Z","timestamp":1784633262000},"source":"Crossref","is-referenced-by-count":0,"title":["MRGBMDAT: a multi-relational graph encoder network with bilinear fusion for miRNA-disease association type prediction"],"prefix":"10.1093","volume":"42","author":[{"given":"Yan","family":"Sun","sequence":"first","affiliation":[{"name":"College of Engineering, Qufu Normal University , Rizhao, Shandong 276826,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenjing","family":"Su","sequence":"additional","affiliation":[{"name":"School of Computer Science, Qufu Normal University , Rizhao, Shandong 276826,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siqi","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Qufu Normal University , Rizhao, Shandong 276826,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shijia","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Computer Science, Qufu Normal University , Rizhao, Shandong 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273165,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin-Xing","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Health and Life Sciences, University of Health and Rehabilitation Sciences , Qingdao, Shandong 266113,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,7,22]]},"reference":[{"key":"2026082813181072800_btag534-B1","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/S0092-8674(04)00045-5","article-title":"MicroRNAs: genomics, biogenesis, mechanism, and function","volume":"116","author":"Bartel","year":"2004","journal-title":"Cell"},{"key":"2026082813181072800_btag534-B2","doi-asserted-by":"crossref","first-page":"e113449","DOI":"10.1371\/journal.pone.0113449","article-title":"MiRNA-1\/133a clusters regulate adrenergic control of cardiac repolarization","volume":"9","author":"Besser","year":"2014","journal-title":"PLoS 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