{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T07:01:03Z","timestamp":1784185263773,"version":"3.55.0"},"reference-count":73,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2022,5,6]],"date-time":"2022-05-06T00:00:00Z","timestamp":1651795200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61801432"],"award-info":[{"award-number":["61801432"]}],"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":["61901103"],"award-info":[{"award-number":["61901103"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005046","name":"Natural Science Foundation of Heilongjiang Province","doi-asserted-by":"publisher","award":["LH2019F002"],"award-info":[{"award-number":["LH2019F002"]}],"id":[{"id":"10.13039\/501100005046","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Postdoctoral Science Foundation of Heilongjiang Province of China","award":["LBH-Z19106"],"award-info":[{"award-number":["LBH-Z19106"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,9,20]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Motivation<\/jats:title><jats:p>In recent years, a large number of biological experiments have strongly shown that miRNAs play an important role in understanding disease pathogenesis. The discovery of miRNA\u2013disease associations is beneficial for disease diagnosis and treatment. Since inferring these associations through biological experiments is time-consuming and expensive, researchers have sought to identify the associations utilizing computational approaches. Graph Convolutional Networks (GCNs), which exhibit excellent performance in link prediction problems, have been successfully used in miRNA\u2013disease association prediction. However, GCNs only consider 1st-order neighborhood information at one layer but fail to capture information from high-order neighbors to learn miRNA and disease representations through information propagation. Therefore, how to aggregate information from high-order neighborhood effectively in an explicit way is still challenging.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>To address such a challenge, we propose a novel method called mixed neighborhood information for miRNA\u2013disease association (MINIMDA), which could fuse mixed high-order neighborhood information of miRNAs and diseases in multimodal networks. First, MINIMDA constructs the integrated miRNA similarity network and integrated disease similarity network respectively with their multisource information. Then, the embedding representations of miRNAs and diseases are obtained by fusing mixed high-order neighborhood information from multimodal network which are the integrated miRNA similarity network, integrated disease similarity network and the miRNA\u2013disease association networks. Finally, we concentrate the multimodal embedding representations of miRNAs and diseases and feed them into the multilayer perceptron (MLP) to predict their underlying associations. Extensive experimental results show that MINIMDA is superior to other state-of-the-art methods overall. Moreover, the outstanding performance on case studies for esophageal cancer, colon tumor and lung cancer further demonstrates the effectiveness of MINIMDA.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>https:\/\/github.com\/chengxu123\/MINIMDA and http:\/\/120.79.173.96\/<\/jats:p><\/jats:sec>","DOI":"10.1093\/bib\/bbac159","type":"journal-article","created":{"date-parts":[[2022,4,14]],"date-time":"2022-04-14T11:16:25Z","timestamp":1649934985000},"source":"Crossref","is-referenced-by-count":93,"title":["Predicting miRNA\u2013disease associations via learning multimodal networks and fusing mixed neighborhood information"],"prefix":"10.1093","volume":"23","author":[{"given":"Zhengzheng","family":"Lou","sequence":"first","affiliation":[{"name":"School of Computer and Artificial Intelligence , Zhengzhou University, Zhengzhou 450000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaoxu","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Computer and Artificial Intelligence , Zhengzhou University, Zhengzhou 450000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer and Artificial Intelligence , Zhengzhou University, Zhengzhou 450000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhixia","family":"Teng","sequence":"additional","affiliation":[{"name":"College of Information and Computer Engineering , Northeast Forestry University, Harbin 150040, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"Departments of Cerebrovascular Diseases , The Second Affiliated Hospital of Zhengzhou University, Zhengzhou 450000, 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