{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T08:46:35Z","timestamp":1775119595533,"version":"3.50.1"},"reference-count":44,"publisher":"Oxford University Press (OUP)","issue":"8","license":[{"start":{"date-parts":[[2022,2,12]],"date-time":"2022-02-12T00:00:00Z","timestamp":1644624000000},"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\/100017054","name":"NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization","doi-asserted-by":"publisher","award":["U1909208"],"award-info":[{"award-number":["U1909208"]}],"id":[{"id":"10.13039\/100017054","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61962050"],"award-info":[{"award-number":["61962050"]}],"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":["62072473"],"award-info":[{"award-number":["62072473"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013314","name":"111 Project","doi-asserted-by":"publisher","award":["B18059"],"award-info":[{"award-number":["B18059"]}],"id":[{"id":"10.13039\/501100013314","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004001","name":"Science and Technology Foundation of Guizhou Province of China","doi-asserted-by":"crossref","award":["2020]1Y264"],"award-info":[{"award-number":["2020]1Y264"]}],"id":[{"id":"10.13039\/501100004001","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,4,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Many studies have shown that microRNAs (miRNAs) play a key role in human diseases. Meanwhile, traditional experimental methods for miRNA\u2013disease association identification are extremely costly, time-consuming and challenging. Therefore, many computational methods have been developed to predict potential associations between miRNAs and diseases. However, those methods mainly predict the existence of miRNA\u2013disease associations, and they cannot predict the deep-level miRNA\u2013disease association types.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In this study, we propose a new end-to-end deep learning method (called PDMDA) to predict deep-level miRNA\u2013disease associations with graph neural networks (GNNs) and miRNA sequence features. Based on the sequence and structural features of miRNAs, PDMDA extracts the miRNA feature representations by a fully connected network (FCN). The disease feature representations are extracted from the disease\u2013gene network and gene\u2013gene interaction network by GNN model. Finally, a multilayer with three fully connected layers and a softmax layer is designed to predict the final miRNA\u2013disease association scores based on the concatenated feature representations of miRNAs and diseases. Note that PDMDA does not take the miRNA\u2013disease association matrix as input to compute the Gaussian interaction profile similarity. We conduct three experiments based on six association type samples (including circulations, epigenetics, target, genetics, known association of which their types are unknown and unknown association samples). We conduct fivefold cross-validation validation to assess the prediction performance of PDMDA. The area under the receiver operating characteristic curve scores is used as metric. The experiment results show that PDMDA can accurately predict the deep-level miRNA\u2013disease associations.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>Data and source codes are available at https:\/\/github.com\/27167199\/PDMDA.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btac077","type":"journal-article","created":{"date-parts":[[2022,2,5]],"date-time":"2022-02-05T12:09:25Z","timestamp":1644062965000},"page":"2226-2234","source":"Crossref","is-referenced-by-count":36,"title":["PDMDA: predicting deep-level miRNA\u2013disease associations with graph neural networks and sequence features"],"prefix":"10.1093","volume":"38","author":[{"given":"Cheng","family":"Yan","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Hunan University of Chinese Medicine , Changsha 410208, China"},{"name":"School of Computer Science and Engineering, Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha 410083, China"}]},{"given":"Guihua","family":"Duan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha 410083, China"}]},{"given":"Na","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha 410083, China"}]},{"given":"Lishen","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha 410083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4593-9332","authenticated-orcid":false,"given":"Fang-Xiang","family":"Wu","sequence":"additional","affiliation":[{"name":"Division of Biomedical Engineering and Department of Mechanical Engineering, University of Saskatchewan , Saskatoon SK S7N5A9, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1516-0480","authenticated-orcid":false,"given":"Jianxin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha 410083, China"}]}],"member":"286","published-online":{"date-parts":[[2022,2,12]]},"reference":[{"key":"2023020109023212400_btac077-B1","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1002\/wics.101","article-title":"Principal component analysis","volume":"2","author":"Abdi","year":"2010","journal-title":"Wiley Interdiscip. 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