{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T09:18:46Z","timestamp":1783156726969,"version":"3.54.6"},"reference-count":68,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100011447","name":"Science and Technology Department of Henan Province","doi-asserted-by":"publisher","award":["252102211041"],"award-info":[{"award-number":["252102211041"]}],"id":[{"id":"10.13039\/501100011447","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013142","name":"Key Research and Development Project of Hainan Province","doi-asserted-by":"publisher","award":["231111212500"],"award-info":[{"award-number":["231111212500"]}],"id":[{"id":"10.13039\/501100013142","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010950","name":"Department of Science and Technology of Henan Province","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100010950","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Engineering Applications of Artificial Intelligence"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.engappai.2026.114970","type":"journal-article","created":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T22:06:47Z","timestamp":1777932407000},"page":"114970","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"P2","title":["Hypergraph regularization and reinforcement learning for micro-ribonucleic acid\u2013disease association prediction"],"prefix":"10.1016","volume":"177","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-2346-0268","authenticated-orcid":false,"given":"Huaibin","family":"Hou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhentao","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong","family":"Jin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"2","key":"10.1016\/j.engappai.2026.114970_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":"10.1016\/j.engappai.2026.114970_b2","series-title":"Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference","first-page":"1601","article-title":"Learning with hypergraphs: clustering, classification and embedding","author":"Bernhard","year":"2007"},{"issue":"7","key":"10.1016\/j.engappai.2026.114970_b3","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1016\/S0031-3203(96)00142-2","article-title":"The use of the area under the roc curve in the evaluation of machine learning algorithms","volume":"30","author":"Bradley","year":"1997","journal-title":"Pattern Recognit."},{"issue":"12","key":"10.1016\/j.engappai.2026.114970_b4","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0168284","article-title":"Identification of MicroRNAs as breast cancer prognosis markers through the cancer genome atlas","volume":"11","author":"Chang","year":"2016","journal-title":"PLoS One."},{"issue":"1\u20132","key":"10.1016\/j.engappai.2026.114970_b5","article-title":"Cross-modal imputation and gated GCN for predicting miRNA-disease association (CIGGNET)","volume":"29","author":"Chen","year":"2025","journal-title":"Int. J. Data Min. Bioinform."},{"issue":"3","key":"10.1016\/j.engappai.2026.114970_b6","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1109\/TCBBIO.2025.3553243","article-title":"PNAGMDA: a principal neighborhood aggregation based graph neural network for miRNA-disease association prediction","volume":"22","author":"Chen","year":"2025","journal-title":"IEEE ACM Trans. Comput. Biol. Bioinf."},{"issue":"S2","key":"10.1016\/j.engappai.2026.114970_b7","first-page":"1","article-title":"NCMCMDA: miRNA-disease association prediction through neighborhood constraint matrix completion","volume":"22","author":"Chen","year":"2020","journal-title":"Briefings Bioinf."},{"issue":"3","key":"10.1016\/j.engappai.2026.114970_b8","article-title":"MicroRNAs and complex diseases: from experimental results to computational models","volume":"22","author":"Chen","year":"2017","journal-title":"Briefings Bioinf."},{"issue":"24","key":"10.1016\/j.engappai.2026.114970_b9","doi-asserted-by":"crossref","first-page":"4256","DOI":"10.1093\/bioinformatics\/bty503","article-title":"Predicting miRNA-disease association based on inductive matrix completion","author":"Chen","year":"2018","journal-title":"Bioinform."},{"key":"10.1016\/j.engappai.2026.114970_b10","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.ymeth.2024.06.007","article-title":"PGCNMDA: learning node representations along paths with graph convolutional network for predicting miRNA-disease associations","volume":"229","author":"Chu","year":"2024","journal-title":"Methods."},{"issue":"suppl 12","key":"10.1016\/j.engappai.2026.114970_b11","article-title":"Identifying miRNA-disease associations based on graph convolutional networks via graph sampling through the feature and topology graph","volume":"22","author":"Chu","year":"2021","journal-title":"Briefings Bioinf."},{"issue":"1","key":"10.1016\/j.engappai.2026.114970_b12","doi-asserted-by":"crossref","DOI":"10.1186\/s12863-024-01293-z","article-title":"Establishing a GRU-GCN coordination-based prediction model for miRNA-disease associations","volume":"26","author":"Chuang","year":"2025","journal-title":"BMC Genom. Data."},{"issue":"D1","key":"10.1016\/j.engappai.2026.114970_b13","doi-asserted-by":"crossref","first-page":"1327","DOI":"10.1093\/nar\/gkad717","article-title":"HMDD v4.0: a database for experimentally supported human microRNA-disease associations","volume":"52","author":"Cui","year":"2024","journal-title":"Nucleic Acids Res."},{"issue":"1","key":"10.1016\/j.engappai.2026.114970_b14","article-title":"A multi-source similarity fusion method based on hypergraph convolutional networks and graph transformers for predicting miRNA-disease associations","volume":"120","author":"Dai","year":"2026","journal-title":"Comput. Biol. Chem."},{"issue":"1","key":"10.1016\/j.engappai.2026.114970_b15","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbab543","article-title":"Predicting miRNA-disease associations using an ensemble learning framework with resampling method","volume":"23","author":"Dai","year":"2021","journal-title":"Briefings Bioinf."},{"issue":"3","key":"10.1016\/j.engappai.2026.114970_b16","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbac079","article-title":"MLRDFM: a multi-view laplacian regularized deepfm model for predicting miRNA-disease associations","volume":"23","author":"Ding","year":"2022","journal-title":"Briefings Bioinf."},{"key":"10.1016\/j.engappai.2026.114970_b17","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.126304","article-title":"C3R: category contrastive adaptation and consistency regularization for cross-modality medical image segmentation","volume":"269","author":"Ding","year":"2025","journal-title":"Expert Syst. Appl."},{"issue":"4","key":"10.1016\/j.engappai.2026.114970_b18","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1007\/s10654-016-0149-3","article-title":"Statistical tests, p values, confidence intervals, and power: a guide to misinterpretations","volume":"31","author":"Greenland","year":"2016","journal-title":"Eur. J. Epidemiol."},{"key":"10.1016\/j.engappai.2026.114970_b19","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.111222","article-title":"Deep gate information bottleneck-based prediction model for complex disease-related micro-ribonucleic acids via heterogeneous biological networks","volume":"156","author":"Guo","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"16","key":"10.1016\/j.engappai.2026.114970_b20","doi-asserted-by":"crossref","first-page":"8105","DOI":"10.1093\/nar\/gky567","article-title":"A deep recurrent neural network discovers complex biological rules to decipher RNA protein-coding potential","volume":"46","author":"Hill","year":"2018","journal-title":"Nucleic Acids Re"},{"issue":"8","key":"10.1016\/j.engappai.2026.114970_b21","doi-asserted-by":"crossref","first-page":"2011","DOI":"10.1109\/TPAMI.2019.2913372","article-title":"Squeeze-and-excitation networks","volume":"42","author":"Hu","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"D1","key":"10.1016\/j.engappai.2026.114970_b22","first-page":"D148","article-title":"MiRTarBase: updates to the experimentally validated microRNA-target interaction database","volume":"48","author":"Huang","year":"2020","journal-title":"Nucleic Acids Res."},{"issue":"D1","key":"10.1016\/j.engappai.2026.114970_b23","doi-asserted-by":"crossref","first-page":"D573","DOI":"10.1093\/nar\/gky1126","article-title":"HumanNet v2: human gene networks for disease research","volume":"47","author":"Hwang","year":"2019","journal-title":"Nucleic Acids Res."},{"issue":"Suppl 1","key":"10.1016\/j.engappai.2026.114970_b24","doi-asserted-by":"crossref","first-page":"S2","DOI":"10.1186\/1752-0509-4-S1-S2","article-title":"Prioritization of disease MicroRNAs through a human phenome-micrornaome network","volume":"4","author":"Jiang","year":"2010","journal-title":"BMC Syst. Biol."},{"issue":"Database","key":"10.1016\/j.engappai.2026.114970_b25","doi-asserted-by":"crossref","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."},{"issue":"5752","key":"10.1016\/j.engappai.2026.114970_b26","doi-asserted-by":"crossref","first-page":"1288","DOI":"10.1126\/science.1121566","article-title":"Encountering MicroRNAs in cell fate signaling","volume":"310","author":"Karp","year":"2005","journal-title":"Sci."},{"key":"10.1016\/j.engappai.2026.114970_b27","first-page":"1","article-title":"Prediction of potential miRNA-disease associations based on a masked graph autoencoder","author":"Ke","year":"2024","journal-title":"IEEE ACM Trans. Comput. Biol. Bioinf."},{"key":"10.1016\/j.engappai.2026.114970_b28","unstructured":"Kingma, D.P., Ba, J., 2014. Adam: a method for stochastic optimization. In: Proceedings of the 3rd International Conference on Learning Representations. ICLR."},{"issue":"D1","key":"10.1016\/j.engappai.2026.114970_b29","doi-asserted-by":"crossref","first-page":"D155","DOI":"10.1093\/nar\/gky1141","article-title":"MiRBase: from microRNA sequences to function","volume":"47","author":"Kozomara","year":"2019","journal-title":"Nucleic Acids Res."},{"issue":"4","key":"10.1016\/j.engappai.2026.114970_b30","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1007\/s13312-011-0055-4","article-title":"Receiver operating characteristic (ROC) curve for medical researchers","volume":"48","author":"Kumar","year":"2011","journal-title":"Indian Pediatr."},{"issue":"4","key":"10.1016\/j.engappai.2026.114970_b31","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbaa350","article-title":"A comprehensive survey on computational methods of non-coding RNA and disease association prediction","volume":"22","author":"Lei","year":"2020","journal-title":"Briefings Bioinf."},{"key":"10.1016\/j.engappai.2026.114970_b32","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.126931","article-title":"TCD-GCN-light: A lightweight temporal-channel decoupling graph convolutional network for human early action prediction based on channel fusion","volume":"275","author":"Li","year":"2025","journal-title":"Expert Syst. Appl."},{"issue":"1","key":"10.1016\/j.engappai.2026.114970_b33","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1109\/TCBBIO.2024.3518515","article-title":"MHMDA: \u201dSimilarity-Association-Similarity\u201d metapaths and heterogeneous-hyper network learning for miRNA-disease association prediction","volume":"22","author":"Li","year":"2025","journal-title":"IEEE ACM Trans. Comput. Biol. Bioinf."},{"key":"10.1016\/j.engappai.2026.114970_b34","doi-asserted-by":"crossref","first-page":"2538","DOI":"10.1093\/bioinformatics\/btz965","article-title":"Neural inductive matrix completion with graph convolutional networks for miRNA-disease association prediction","volume":"36","author":"Li","year":"2020","journal-title":"Bioinformatics"},{"issue":"7","key":"10.1016\/j.engappai.2026.114970_b35","doi-asserted-by":"crossref","first-page":"1722","DOI":"10.1158\/1078-0432.CCR-10-1800","article-title":"Analysis of mir-195 and mir-497 expression, regulation and role in breast cancer","volume":"17","author":"Li","year":"2011","journal-title":"Clin. Cancer Res.: An Off. J. Am. Assoc. Cancer Res."},{"issue":"4","key":"10.1016\/j.engappai.2026.114970_b36","first-page":"1775","article-title":"Hierarchical graph attention network for miRNA-disease association prediction","volume":"30","author":"Li","year":"2022","journal-title":"Molther."},{"issue":"10113","key":"10.1016\/j.engappai.2026.114970_b37","article-title":"Applications of artificial intelligence to aid detection of dementia: a scoping review on current capabilities and future directions","volume":"127","author":"Li","year":"2022","journal-title":"J. Biomed. Inf."},{"issue":"3","key":"10.1016\/j.engappai.2026.114970_b38","first-page":"265","article-title":"Medical subject headings (MeSH)","volume":"88","author":"Lipscomb","year":"2000","journal-title":"Bull. Med. Libr. Assoc."},{"issue":"4","key":"10.1016\/j.engappai.2026.114970_b39","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0062383","article-title":"MicroRNA-449a enhances radiosensitivity in CL1-0 lung adenocarcinoma cells","volume":"8","author":"Liu","year":"2013","journal-title":"Plos One."},{"issue":"2","key":"10.1016\/j.engappai.2026.114970_b40","article-title":"Predicting miRNA-disease associations via learning multimodal networks and fusing mixed neighborhood information","volume":"23","author":"Lou","year":"2022","journal-title":"Briefings Bioinf."},{"key":"10.1016\/j.engappai.2026.114970_b41","doi-asserted-by":"crossref","DOI":"10.1016\/j.compeleceng.2025.110242","article-title":"Gene-related multi-network collaborative deep feature learning for predicting miRNA-disease associations","volume":"123","author":"Lu","year":"2025","journal-title":"Comput. Electr. Eng."},{"issue":"2","key":"10.1016\/j.engappai.2026.114970_b42","doi-asserted-by":"crossref","first-page":"769","DOI":"10.1109\/TCBBIO.2025.3536039","article-title":"Hemdap: heterogeneous graph self-supervised learning for miRNA-disease association prediction","volume":"22","author":"Ma","year":"2025","journal-title":"IEEE ACM Trans. Comput. Biol. Bioinf."},{"issue":"7","key":"10.1016\/j.engappai.2026.114970_b43","first-page":"544","article-title":"The art and design of genetic screens: RNA interference","volume":"9","author":"Michael","year":"2008","journal-title":"Nat. Rev. Genet."},{"issue":"5","key":"10.1016\/j.engappai.2026.114970_b44","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1016\/j.gde.2005.08.005","article-title":"How MicroRNAs control cell division, differentiation and death","volume":"15","author":"Miska","year":"2005","journal-title":"Curr. Opin. Genet. Dev."},{"key":"10.1016\/j.engappai.2026.114970_b45","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1038\/nature14236","article-title":"Human-level control through deep reinforcement","volume":"518","author":"Mnih","year":"2015","journal-title":"Nat."},{"issue":"3","key":"10.1016\/j.engappai.2026.114970_b46","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1016\/0022-2836(70)90057-4","article-title":"A general method applicable to the search for similarities in the amino acid sequence of two proteins","volume":"48","author":"Needleman","year":"1970","journal-title":"J. Mol. Biol."},{"key":"10.1016\/j.engappai.2026.114970_b47","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.113023","article-title":"Automtnas: automated meta-reinforcement learning on graph tokenization for graph neural architecture search","volume":"310","author":"Nie","year":"2025","journal-title":"Knowl. -Based Syst."},{"issue":"2","key":"10.1016\/j.engappai.2026.114970_b48","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbad094","article-title":"AMHMDA: attention aware multi-view similarity networks and hypergraph learning for miRNA-disease associations identification","volume":"24","author":"Ning","year":"2023","journal-title":"Briefings Bioinf."},{"key":"10.1016\/j.engappai.2026.114970_b49","doi-asserted-by":"crossref","unstructured":"Peng, Z., 2020. Graph representation learning via graphical mutual information maximization. In: Proc. Web Conf. pp. 259\u2013270.","DOI":"10.1145\/3366423.3380112"},{"issue":"1","key":"10.1016\/j.engappai.2026.114970_b50","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbad524","article-title":"MHCLMDA: Multihypergraphcontrastive learning for miRNA-disease association prediction","volume":"25","author":"Peng","year":"2024","journal-title":"Briefings Bioinf."},{"issue":"D1","key":"10.1016\/j.engappai.2026.114970_b51","first-page":"D845","article-title":"The DisGeNET knowledge platform for disease genomics: update","volume":"48","author":"Pi\u00f1ero","year":"2019","journal-title":"Nucleic Acids Res."},{"issue":"1","key":"10.1016\/j.engappai.2026.114970_b52","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1186\/1752-0509-7-101","article-title":"Walking the interactome to identify human miRNA-disease associations through the functional link between miRNA targets and disease genes","volume":"7","author":"Shi","year":"2013","journal-title":"BMC Syst. Biol."},{"issue":"33","key":"10.1016\/j.engappai.2026.114970_b53","doi-asserted-by":"crossref","first-page":"12481","DOI":"10.1073\/pnas.0605298103","article-title":"NF-k B-dependent induction of microRNA mir-146, an inhibitor targeted to signaling proteins of innate immune responses","volume":"103","author":"Taganov","year":"2006","journal-title":"Proc. Natl. Acad. Sci. U. S. Am."},{"issue":"6","key":"10.1016\/j.engappai.2026.114970_b54","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbab174","article-title":"Multi-view multichannel attention graph convolutional network for miRNA-disease association prediction","volume":"22","author":"Tang","year":"2021","journal-title":"Briefings Bioinf."},{"key":"10.1016\/j.engappai.2026.114970_b55","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbae168","article-title":"MGCNSS: miRNA-disease association prediction with multi-layer graph convolution and distance-based negative sample selection strategy","volume":"25","author":"Tian","year":"2024","journal-title":"Briefings Bioinf."},{"issue":"1","key":"10.1016\/j.engappai.2026.114970_b56","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbac495","article-title":"Predicting miRNA-disease associations based on lncrna-miRNA interactions and graph convolution networks","volume":"24","author":"Wang","year":"2023","journal-title":"Briefings Bioinf."},{"issue":"2","key":"10.1016\/j.engappai.2026.114970_b57","doi-asserted-by":"crossref","first-page":"911","DOI":"10.1109\/TCBBIO.2025.3543643","article-title":"Predicting miRNA-disease associations via meta-path embedding","volume":"22","author":"Wang","year":"2025","journal-title":"IEEE ACM Trans. Comput. Biol. Bioinf."},{"key":"10.1016\/j.engappai.2026.114970_b58","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.125240","article-title":"The structure-sharing hypergraph reasoning attention module for cnns","volume":"259","author":"Wang","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.engappai.2026.114970_b59","doi-asserted-by":"crossref","first-page":"1644","DOI":"10.1093\/bioinformatics\/btq241","article-title":"Inferring the human microRNA functional similarity and functional network based on microRNA-associated diseases","volume":"26","author":"Wang","year":"2010","journal-title":"Bioinformatics"},{"issue":"4","key":"10.1016\/j.engappai.2026.114970_b60","doi-asserted-by":"crossref","first-page":"815","DOI":"10.1109\/TCBB.2014.2361348","article-title":"ClusterViz: a cytoscape app for cluster analysis of biological network","volume":"12","author":"Wang","year":"2015","journal-title":"IEEE ACM Trans. Comput. Biol. Bioinf."},{"key":"10.1016\/j.engappai.2026.114970_b61","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J., Kweon, I., et al., 2018. CBAM: convolutional block attention module. In: Proceedings of the European Conference on Computer Vision. ECCV.","DOI":"10.1007\/978-3-030-01234-2_1"},{"issue":"2","key":"10.1016\/j.engappai.2026.114970_b62","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1093\/bioinformatics\/btx545","article-title":"A graph regularized nonnegative matrix factorization method for identifying microRNA-disease associations","volume":"34","author":"Xiao","year":"2017","journal-title":"Bioinformatics"},{"issue":"8","key":"10.1016\/j.engappai.2026.114970_b63","doi-asserted-by":"crossref","DOI":"10.1371\/annotation\/28592478-72f5-4937-919b-b2342d6ceda0","article-title":"Prediction of MicroRNAs associated with human diseases based on weighted k most similar neighbors","volume":"8","author":"Xuan","year":"2013","journal-title":"Plos One."},{"issue":"D1","key":"10.1016\/j.engappai.2026.114970_b64","doi-asserted-by":"crossref","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."},{"issue":"2","key":"10.1016\/j.engappai.2026.114970_b65","doi-asserted-by":"crossref","first-page":"1308","DOI":"10.1109\/TCBB.2022.3170843","article-title":"Predicting miRNA-disease associations via node-level attention graph autoencoder","volume":"20","author":"Zhang","year":"2023","journal-title":"IEEE ACM Trans. Comput. Biol."},{"issue":"13","key":"10.1016\/j.engappai.2026.114970_b66","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.7150\/ijms.27341","article-title":"Biology of mir-17-92 cluster and its progress in lung cancer","volume":"15","author":"Zhang","year":"2018","journal-title":"Int. J. Med. Sci."},{"issue":"5","key":"10.1016\/j.engappai.2026.114970_b67","doi-asserted-by":"crossref","first-page":"1733","DOI":"10.1109\/TCBB.2020.3013837","article-title":"MISSIM: an incremental learning-based model with applications to the prediction of miRNA-disease association","volume":"18","author":"Zheng","year":"2021","journal-title":"IEEE ACM Trans. Comput. Biol. Bioinf."},{"issue":"9","key":"10.1016\/j.engappai.2026.114970_b68","doi-asserted-by":"crossref","first-page":"5570","DOI":"10.1109\/TNNLS.2021.3129772","article-title":"Predicting miRNA-disease associations through deep autoencoder with multiple kernel learning","volume":"34","author":"Zhou","year":"2023","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."}],"container-title":["Engineering Applications of Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626012522?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626012522?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T08:47:36Z","timestamp":1783154856000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0952197626012522"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":68,"alternative-id":["S0952197626012522"],"URL":"https:\/\/doi.org\/10.1016\/j.engappai.2026.114970","relation":{},"ISSN":["0952-1976"],"issn-type":[{"value":"0952-1976","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Hypergraph regularization and reinforcement learning for micro-ribonucleic acid\u2013disease association prediction","name":"articletitle","label":"Article Title"},{"value":"Engineering Applications of Artificial Intelligence","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.engappai.2026.114970","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"114970"}}