{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T06:26:58Z","timestamp":1786602418631,"version":"build-2736575974"},"reference-count":22,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T00:00:00Z","timestamp":1786579200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Bioinform."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>MicroRNAs (miRNAs) regulate gene expression and are closely linked to the onset and progression of immune\u2011related diseases. Experimental discovery of disease\u2011associated miRNAs remains costly and time\u2011consuming, motivating computational prioritization. However, existing matrix\u2011completion and deep graph\u2011learning methods either underuse local biological neighborhood evidence or require complex multi\u2011view neural architectures.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this study, we present RLNSF\u2011MDA, a reliability\u2011guided graph\u2011regularized logistic matrix factorization framework for predicting potential miRNA\u2013disease associations, with an emphasis on immune disease analysis. The method integrates disease semantic similarity; miRNA functional, semantic, and sequence similarities; and training\u2011only Gaussian interaction profile similarities through data\u2011driven reliability weights. It then combines multi\u2011scale neighborhood evidence, diffusion scores, low\u2011rank reconstruction features, and contrastive graph\u2011regularized latent factors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>On the HMDD v3.2 benchmark containing 788 miRNAs and 374 diseases, RLNSF\u2011MDA achieved an average accuracy of 0.8525, an AUC of 0.9194, and an AUPR of 0.9055 in five\u2011fold cross\u2011validation, and an average accuracy of 0.8569, an AUC of 0.9266, and an AUPR of 0.9197 in ten\u2011fold cross\u2011validation. Ablation experiments showed that the full model outperformed reduced feature combinations and training strategies, supporting the contribution of reliability\u2011guided fusion, side\u2011score construction, graph regularization, and contrastive ranking. Evidence from immune\u2011related case studies in dbDEMC V2.0 and miR2Disease further covered lupus nephritis, lymphoma, and leukemia, with 45, 47, and 47 confirmed miRNAs, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>These results suggest that RLNSF\u2011MDA provides an effective and interpretable framework for prioritizing candidate miRNAs associated with immune\u2011related diseases.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fbinf.2026.1926188","type":"journal-article","created":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T05:31:57Z","timestamp":1786599117000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["RLNSF-MDA: reliability-guided graph-regularized matrix factorization for immune-related miRNA\u2013disease association prediction"],"prefix":"10.3389","volume":"6","author":[{"given":"Xin","family":"Li","sequence":"first","affiliation":[{"name":"Department of Immunology, School of Basic Medical Science, Central South University","place":["Changsha, Hunan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaoyu","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science, The University of Sydney","place":["Sydney, NSW, Australia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Immunology, School of Basic Medical Science, Central South University","place":["Changsha, Hunan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,13]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","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":"B2","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1016\/j.cell.2009.01.002","article-title":"MicroRNAs: target recognition and regulatory functions","volume":"136","author":"Bartel","year":"2009","journal-title":"Cell"},{"key":"B3","doi-asserted-by":"publisher","first-page":"5501","DOI":"10.1038\/srep05501","article-title":"Semi-supervised learning for potential human microRNA-disease associations inference","volume":"4","author":"Chen","year":"2014","journal-title":"Sci. Rep."},{"key":"B4","doi-asserted-by":"publisher","first-page":"21106","DOI":"10.1038\/srep21106","article-title":"WBSMDA: within and between score for MiRNA-Disease association prediction","volume":"6","author":"Chen","year":"","journal-title":"Sci. Rep."},{"key":"B5","doi-asserted-by":"publisher","first-page":"65257","DOI":"10.18632\/oncotarget.11251","article-title":"HGIMDA: heterogeneous graph inference for miRNA-disease association prediction","volume":"7","author":"Chen","year":"","journal-title":"Oncotarget"},{"key":"B6","doi-asserted-by":"publisher","first-page":"952","DOI":"10.1080\/15476286.2017.1312226","article-title":"RKNNMDA: ranking-based KNN for MiRNA-Disease association prediction","volume":"14","author":"Chen","year":"2017","journal-title":"RNA Biol."},{"key":"B7","doi-asserted-by":"publisher","first-page":"4256","DOI":"10.1093\/bioinformatics\/bty503","article-title":"Predicting miRNA-disease association based on inductive matrix completion","volume":"34","author":"Chen","year":"2018","journal-title":"Bioinformatics"},{"key":"B8","doi-asserted-by":"publisher","first-page":"446","DOI":"10.1109\/jbhi.2021.3088342","article-title":"Predicting miRNA-Disease associations based on multi-view variational graph auto-encoder with matrix factorization","volume":"26","author":"Ding","year":"2022","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"B9","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1186\/s13059-019-1811-3","article-title":"Benchmark of computational methods for predicting microRNA-disease associations","volume":"20","author":"Huang","year":"","journal-title":"Genome Biol."},{"key":"B10","doi-asserted-by":"publisher","first-page":"D1013","DOI":"10.1093\/nar\/gky1010","article-title":"HMDD v3.0: a database for experimentally supported human microRNA-disease associations","volume":"47","author":"Huang","year":"","journal-title":"Nucleic Acids Res."},{"key":"B11","doi-asserted-by":"publisher","first-page":"6658","DOI":"10.1038\/s41598-020-63735-9","article-title":"Predicting mirna-disease association from heterogeneous information network with grarep embedding model","volume":"10","author":"Ji","year":"","journal-title":"Sci. Reports"},{"key":"B12","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1186\/s12967-020-02490-x","article-title":"Prediction of drug-target interactions from multi-molecular network based on line network representation method","volume":"18","author":"Ji","year":"","journal-title":"J. Transl. Med."},{"key":"B13","doi-asserted-by":"publisher","first-page":"bbae167","DOI":"10.1093\/bib\/bbae167","article-title":"MUSCLE: multi-view and multi-scale attentional feature fusion for microRNA-disease associations prediction","volume":"25","author":"Ji","year":"2024","journal-title":"Briefings Bioinforma."},{"key":"B14","doi-asserted-by":"publisher","first-page":"6161","DOI":"10.1038\/s41467-026-72882-y","article-title":"Captain: a multimodal foundation model pretrained on co-assayed single-cell rna and protein","volume":"17","author":"Ji","year":"2026","journal-title":"Nat. Commun"},{"key":"B15","doi-asserted-by":"publisher","first-page":"D98","DOI":"10.1093\/nar\/gkn714","article-title":"miR2Disease: a manually curated database for microRNA deregulation in human disease","volume":"37","author":"Jiang","year":"2009","journal-title":"Nucleic Acids Res."},{"key":"B16","doi-asserted-by":"publisher","first-page":"21187","DOI":"10.18632\/oncotarget.15061","article-title":"MCMDA: matrix completion for MiRNA-disease association prediction","volume":"8","author":"Li","year":"2017","journal-title":"Oncotarget"},{"key":"B17","doi-asserted-by":"publisher","first-page":"bbaf444","DOI":"10.1093\/bib\/bbaf444","article-title":"Identifying potential miRNA-disease associations through an accurate matrix completion approach","volume":"26","author":"Li","year":"2025","journal-title":"Briefings Bioinforma."},{"key":"B18","doi-asserted-by":"publisher","first-page":"3943","DOI":"10.1109\/access.2017.2754409","article-title":"KATZMDA: prediction of miRNA-Disease associations based on KATZ model","volume":"6","author":"Qu","year":"2018","journal-title":"IEEE Access"},{"key":"B19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2017\/2498957","article-title":"miRNA-Disease association prediction with collaborative matrix factorization","volume":"2017","author":"Shen","year":"2017","journal-title":"Complexity"},{"key":"B20","doi-asserted-by":"publisher","first-page":"3036","DOI":"10.1093\/bioinformatics\/btr500","article-title":"Gaussian interaction profile kernels for predicting drug-target interaction","volume":"27","author":"van Laarhoven","year":"2011","journal-title":"Bioinformatics"},{"key":"B21","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1093\/bioinformatics\/btx545","article-title":"A graph regularized non-negative matrix factorization method for identifying microRNA-disease associations","volume":"34","author":"Xiao","year":"2018","journal-title":"Bioinformatics"},{"key":"B22","doi-asserted-by":"publisher","first-page":"D812","DOI":"10.1093\/nar\/gkw1079","article-title":"dbDEMC 2.0: updated database of differentially expressed miRNAs in human cancers","volume":"45","author":"Yang","year":"2017","journal-title":"Nucleic Acids Res."}],"container-title":["Frontiers in Bioinformatics"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fbinf.2026.1926188\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T05:31:57Z","timestamp":1786599117000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fbinf.2026.1926188\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,13]]},"references-count":22,"alternative-id":["10.3389\/fbinf.2026.1926188"],"URL":"https:\/\/doi.org\/10.3389\/fbinf.2026.1926188","relation":{},"ISSN":["2673-7647"],"issn-type":[{"value":"2673-7647","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,13]]},"article-number":"1926188"}}