{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T15:05:58Z","timestamp":1768921558926,"version":"3.49.0"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,12,8]],"date-time":"2024-12-08T00:00:00Z","timestamp":1733616000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,8]],"date-time":"2024-12-08T00:00:00Z","timestamp":1733616000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 62472202"],"award-info":[{"award-number":["No. 62472202"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Yunnan Province of China","award":["No.2019FA024"],"award-info":[{"award-number":["No.2019FA024"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Health Inf Sci Syst"],"DOI":"10.1007\/s13755-024-00319-1","type":"journal-article","created":{"date-parts":[[2024,12,8]],"date-time":"2024-12-08T01:47:46Z","timestamp":1733622466000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Prediction of miRNA-disease association based on heterogeneous hypergraph convolution and heterogeneous graph multi-scale convolution"],"prefix":"10.1007","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3093-6454","authenticated-orcid":false,"given":"Wei","family":"Dai","sequence":"first","affiliation":[]},{"given":"Sifan","family":"Pang","sequence":"additional","affiliation":[]},{"given":"Zhichen","family":"He","sequence":"additional","affiliation":[]},{"given":"Xiaodong","family":"Fu","sequence":"additional","affiliation":[]},{"given":"Li","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Lijun","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Ning","family":"Yu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,8]]},"reference":[{"issue":"4","key":"319_CR1","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/S1672-0229(08)60044-3","volume":"7","author":"Y Cai","year":"2009","unstructured":"Cai Y, Yu X, Hu S, Yu J. A brief review on the mechanisms of miRNA regulation. Genom Proteom Bioinform. 2009;7(4):147\u201354. https:\/\/doi.org\/10.1016\/S1672-0229(08)60044-3.","journal-title":"Genom Proteom Bioinform"},{"issue":"5","key":"319_CR2","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1016\/j.gde.2005.08.005","volume":"15","author":"EA Miska","year":"2005","unstructured":"Miska EA. How microRNAs control cell division, differentiation and death. Curr Opin Genet Dev. 2005;15(5):563\u20138. https:\/\/doi.org\/10.1016\/j.gde.2005.08.005.","journal-title":"Curr Opin Genet Dev"},{"issue":"20","key":"319_CR3","doi-asserted-by":"publisher","first-page":"5602","DOI":"10.1158\/1078-0432.CCR-13-1326","volume":"19","author":"H Tang","year":"2013","unstructured":"Tang H, Deng M, Tang Y, Xie X, Guo J, Kong Y, Ye F, Su Q, Xie X. miR-200b and miR-200c as prognostic factors and mediators of gastric cancer cell progression. Clin Cancer Res. 2013;19(20):5602\u201312. https:\/\/doi.org\/10.1158\/1078-0432.CCR-13-1326.","journal-title":"Clin Cancer Res"},{"issue":"16","key":"319_CR4","doi-asserted-by":"publisher","first-page":"4762","DOI":"10.7150\/jca.45684","volume":"11","author":"Y Liu","year":"2020","unstructured":"Liu Y, Li Q, Dai Y, Jiang T, Zhou Y. miR-532-3p inhibits proliferation and promotes apoptosis of lymphoma cells by targeting $$\\beta$$-catenin. J Cancer. 2020;11(16):4762. https:\/\/doi.org\/10.7150\/jca.45684.","journal-title":"J Cancer"},{"issue":"3","key":"319_CR5","doi-asserted-by":"publisher","first-page":"604","DOI":"10.1016\/j.ejca.2012.09.031","volume":"49","author":"M Yang","year":"2013","unstructured":"Yang M, Shen H, Qiu C, Ni Y, Wang L, Dong W, Liao Y, Du J. High expression of miR-21 and miR-155 predicts recurrence and unfavourable survival in non-small cell lung cancer. Eur J Cancer. 2013;49(3):604\u201315. https:\/\/doi.org\/10.1016\/j.ejca.2012.09.031.","journal-title":"Eur J Cancer"},{"issue":"4","key":"319_CR6","doi-asserted-by":"publisher","first-page":"728","DOI":"10.1109\/TNB.2023.3275178","volume":"22","author":"X Tang","year":"2023","unstructured":"Tang X, Ji L. Predicting plant miRNA-lncRNA interactions via a deep learning method. IEEE Trans Nanobiosci. 2023;22(4):728\u201333. https:\/\/doi.org\/10.1109\/TNB.2023.3275178.","journal-title":"IEEE Trans Nanobiosci"},{"issue":"4","key":"319_CR7","doi-asserted-by":"publisher","first-page":"280","DOI":"10.26599\/BDMA.2020.9020025","volume":"3","author":"Y Zhang","year":"2020","unstructured":"Zhang Y, Lei X, Fang Z, Pan Y. CircRNA-disease associations prediction based on metapath2vec++ and matrix factorization. Big Data Min Anal. 2020;3(4):280\u201391. https:\/\/doi.org\/10.26599\/BDMA.2020.9020025.","journal-title":"Big Data Min Anal"},{"key":"319_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-020-03765-2","volume":"21","author":"L Zhang","year":"2020","unstructured":"Zhang L, Liu B, Li Z, Zhu X, Liang Z, An J. Predicting MiRNA-disease associations by multiple meta-paths fusion graph embedding model. BMC Bioinform. 2020;21:1\u201319 (10.1186\/s12859-020-03765-2).","journal-title":"BMC Bioinform"},{"key":"319_CR9","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.ymeth.2017.05.024","volume":"124","author":"W Peng","year":"2017","unstructured":"Peng W, Lan W, Zhong J, Wang J, Pan Y. A novel method of predicting microRNA-disease associations based on microRNA, disease, gene and environment factor networks. Methods. 2017;124:69\u201377. https:\/\/doi.org\/10.1016\/j.ymeth.2017.05.024.","journal-title":"Methods"},{"issue":"2","key":"319_CR10","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1109\/TNB.2016.2633276","volume":"16","author":"W Peng","year":"2016","unstructured":"Peng W, Lan W, Yu Z, Wang J, Pan Y. A framework for integrating multiple biological networks to predict MicroRNA-disease associations. IEEE Trans Nanobiosci. 2016;16(2):100\u20137. https:\/\/doi.org\/10.1109\/TNB.2016.2633276.","journal-title":"IEEE Trans Nanobiosci"},{"key":"319_CR11","doi-asserted-by":"publisher","first-page":"1316","DOI":"10.3389\/fgene.2019.01316","volume":"10","author":"L Yu","year":"2020","unstructured":"Yu L, Shen X, Zhong D, Yang J. Three-layer heterogeneous network combined with unbalanced random walk for miRNA-disease association prediction. Front Genet. 2020;10:1316. https:\/\/doi.org\/10.3389\/fgene.2019.01316.","journal-title":"Front Genet"},{"key":"319_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12967-017-1340-3","volume":"15","author":"X Chen","year":"2017","unstructured":"Chen X, Niu Y-W, Wang G-H, Yan G-Y. MKRMDA: multiple kernel learning-based Kronecker regularized least squares for MiRNA-disease association prediction. J Transl Med. 2017;15:1\u201314. https:\/\/doi.org\/10.1186\/s12967-017-1340-3.","journal-title":"J Transl Med"},{"issue":"2","key":"319_CR13","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1093\/bioinformatics\/btx545","volume":"34","author":"Q Xiao","year":"2018","unstructured":"Xiao Q, Luo J, Liang C, Cai J, Ding P. A graph regularized non-negative matrix factorization method for identifying microRNA-disease associations. Bioinformatics. 2018;34(2):239\u201348. https:\/\/doi.org\/10.1093\/bioinformatics\/btx545.","journal-title":"Bioinformatics"},{"issue":"4","key":"319_CR14","doi-asserted-by":"publisher","first-page":"881","DOI":"10.3390\/cells9040881","volume":"9","author":"J Ha","year":"2020","unstructured":"Ha J, Park C, Park C, Park S. Improved prediction of miRNA-disease associations based on matrix completion with network regularization. Cells. 2020;9(4):881. https:\/\/doi.org\/10.3390\/cells9040881.","journal-title":"Cells"},{"issue":"8","key":"319_CR15","doi-asserted-by":"publisher","first-page":"2538","DOI":"10.1093\/bioinformatics\/btz965","volume":"36","author":"J Li","year":"2020","unstructured":"Li J, Zhang S, Liu T, Ning C, Zhang Z, Zhou W. Neural inductive matrix completion with graph convolutional networks for miRNA-disease association prediction. Bioinformatics. 2020;36(8):2538\u201346. https:\/\/doi.org\/10.1093\/bioinformatics\/btz965.","journal-title":"Bioinformatics"},{"issue":"12","key":"319_CR16","doi-asserted-by":"publisher","first-page":"1009655","DOI":"10.1371\/journal.pcbi.1009655","volume":"17","author":"L Li","year":"2021","unstructured":"Li L, Wang Y-T, Ji C-M, Zheng C-H, Ni J-C, Su Y-S. GCAEMDA: Predicting miRNA-disease associations via graph convolutional autoencoder. PLoS Comput Biol. 2021;17(12):1009655. https:\/\/doi.org\/10.1371\/journal.pcbi.1009655.","journal-title":"PLoS Comput Biol"},{"key":"319_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106069","volume":"149","author":"N Ai","year":"2022","unstructured":"Ai N, Liang Y, Yuan H-L, Ou-Yang D, Liu X-Y, Xie S-L, Ji Y-H. MHDMF: prediction of miRNA-disease associations based on deep matrix factorization with multi-source graph convolutional network. Comput Biol Med. 2022;149: 106069. https:\/\/doi.org\/10.1016\/j.compbiomed.2022.106069.","journal-title":"Comput Biol Med"},{"issue":"6","key":"319_CR18","doi-asserted-by":"publisher","first-page":"3363","DOI":"10.1109\/TCBB.2022.3187739","volume":"20","author":"W Peng","year":"2022","unstructured":"Peng W, Che Z, Dai W, Wei S, Lan W. Predicting miRNA-disease associations from miRNA-gene-disease heterogeneous network with multi-relational graph convolutional network model. IEEE\/ACM Trans Comput Biol Bioinform. 2022;20(6):3363\u201375. https:\/\/doi.org\/10.1109\/TCBB.2022.3187739.","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"issue":"D1","key":"319_CR19","doi-asserted-by":"publisher","first-page":"1013","DOI":"10.1093\/nar\/gky1010","volume":"47","author":"Z Huang","year":"2019","unstructured":"Huang Z, Shi J, Gao Y, Cui C, Zhang S, Li J, Zhou Y, Cui Q. HMDD v3.0: a database for experimentally supported human microRNA-disease associations. Nucleic Acids Res. 2019;47(D1):1013\u20137. https:\/\/doi.org\/10.1093\/nar\/gky1010.","journal-title":"Nucleic Acids Res"},{"issue":"D1","key":"319_CR20","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1093\/nar\/gkz896","volume":"48","author":"H-Y Huang","year":"2020","unstructured":"Huang H-Y, Lin Y-C-D, Li J, Huang K-Y, Shrestha S, Hong H-C, Tang Y, Chen Y-G, Jin C-N, Yu Y, et al. miRTarBase 2020: updates to the experimentally validated microRNA-target interaction database. Nucleic Acids Res. 2020;48(D1):148\u201354. https:\/\/doi.org\/10.1093\/nar\/gkz896.","journal-title":"Nucleic Acids Res"},{"key":"319_CR21","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkw943","author":"J Pi\u00f1ero","year":"2016","unstructured":"Pi\u00f1ero J, Bravo \u00c0, Queralt-Rosinach N, Guti\u00e9rrez-Sacrist\u00e1n A, Deu-Pons J, Centeno E, Garc\u00eda-Garc\u00eda J, Sanz F, Furlong LI. DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants. Nucleic Acids Res. 2016. https:\/\/doi.org\/10.1093\/nar\/gkw943.","journal-title":"Nucleic Acids Res"},{"issue":"D1","key":"319_CR22","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1093\/nar\/gkt1181","volume":"42","author":"A Kozomara","year":"2014","unstructured":"Kozomara A, Griffiths-Jones S. miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res. 2014;42(D1):68\u201373. https:\/\/doi.org\/10.1093\/nar\/gkt1181.","journal-title":"Nucleic Acids Res"},{"issue":"14","key":"319_CR23","doi-asserted-by":"publisher","first-page":"1103","DOI":"10.1001\/jama.1994.03510380059038","volume":"271","author":"HJ Lowe","year":"1994","unstructured":"Lowe HJ, Barnett GO. Understanding and using the medical subject headings (MeSH) vocabulary to perform literature searches. JAMA. 1994;271(14):1103\u20138.","journal-title":"JAMA"},{"issue":"3","key":"319_CR24","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1016\/0022-2836(70)90057-4","volume":"48","author":"SB Needleman","year":"1970","unstructured":"Needleman SB, Wunsch CD. A general method applicable to the search for similarities in the amino acid sequence of two proteins. J Mol Biol. 1970;48(3):443\u201353. https:\/\/doi.org\/10.1016\/0022-2836(70)90057-4.","journal-title":"J Mol Biol"},{"issue":"5","key":"319_CR25","doi-asserted-by":"publisher","first-page":"292","DOI":"10.1093\/bib\/bbac292","volume":"23","author":"W Wang","year":"2022","unstructured":"Wang W, Chen H. Predicting miRNA-disease associations based on graph attention networks and dual Laplacian regularized least squares. Brief Bioinform. 2022;23(5):292. https:\/\/doi.org\/10.1093\/bib\/bbac292.","journal-title":"Brief Bioinform"},{"issue":"1","key":"319_CR26","doi-asserted-by":"publisher","first-page":"446","DOI":"10.1109\/JBHI.2021.3088342","volume":"26","author":"Y Ding","year":"2021","unstructured":"Ding Y, Lei X, Liao B, Wu F-X. Predicting miRNA-disease associations based on multi-view variational graph auto-encoder with matrix factorization. IEEE J Biomed Health Inform. 2021;26(1):446\u201357. https:\/\/doi.org\/10.1109\/JBHI.2021.3088342.","journal-title":"IEEE J Biomed Health Inform"},{"issue":"2","key":"319_CR27","doi-asserted-by":"publisher","first-page":"623","DOI":"10.1093\/bib\/bbac623","volume":"24","author":"X Ruan","year":"2023","unstructured":"Ruan X, Jiang C, Lin P, Lin Y, Liu J, Huang S, Liu X. MSGCL: inferring miRNA-disease associations based on multi-view self-supervised graph structure contrastive learning. Brief Bioinform. 2023;24(2):623. https:\/\/doi.org\/10.1093\/bib\/bbac623.","journal-title":"Brief Bioinform"},{"issue":"2","key":"319_CR28","doi-asserted-by":"publisher","first-page":"094","DOI":"10.1093\/bib\/bbad094","volume":"24","author":"Q Ning","year":"2023","unstructured":"Ning Q, Zhao Y, Gao J, Chen C, Li X, Li T, Yin M. AMHMDA: attention aware multi-view similarity networks and hypergraph learning for miRNA-disease associations identification. Brief Bioinform. 2023;24(2):094. https:\/\/doi.org\/10.1093\/bib\/bbad094.","journal-title":"Brief Bioinform"},{"issue":"3","key":"319_CR29","doi-asserted-by":"publisher","first-page":"079","DOI":"10.1093\/bib\/bbac079","volume":"23","author":"Y Ding","year":"2022","unstructured":"Ding Y, Lei X, Liao B, Wu F-X. MLRDFM: a multi-view Laplacian regularized DeepFM model for predicting miRNA-disease associations. Brief Bioinform. 2022;23(3):079. https:\/\/doi.org\/10.1093\/bib\/bbac079.","journal-title":"Brief Bioinform"},{"key":"319_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2164-11-S4-S5","volume":"11","author":"Z Yang","year":"2010","unstructured":"Yang Z, Ren F, Liu C, He S, Sun G, Gao Q, Yao L, Zhang Y, Miao R, Cao Y, et al. dbDEMC: a database of differentially expressed miRNAs in human cancers. BMC Genom. 2010;11:1\u20138. https:\/\/doi.org\/10.1186\/1471-2164-11-S4-S5","journal-title":"BMC Genom"}],"container-title":["Health Information Science and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13755-024-00319-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13755-024-00319-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13755-024-00319-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T09:08:48Z","timestamp":1765444128000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13755-024-00319-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,8]]},"references-count":30,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["319"],"URL":"https:\/\/doi.org\/10.1007\/s13755-024-00319-1","relation":{},"ISSN":["2047-2501"],"issn-type":[{"value":"2047-2501","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,8]]},"assertion":[{"value":"8 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 November 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 December 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that there are no Conflict of interest to this work.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The all datasets used in this work are all from public databases and don\u2019t require ethical approval.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"4"}}