{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T17:08:06Z","timestamp":1774026486518,"version":"3.50.1"},"reference-count":47,"publisher":"Oxford University Press (OUP)","issue":"13","license":[{"start":{"date-parts":[[2019,12,3]],"date-time":"2019-12-03T00:00:00Z","timestamp":1575331200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Awardee of the NSFC Excellent Young Scholars Program","award":["61722212"],"award-info":[{"award-number":["61722212"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61873212"],"award-info":[{"award-number":["61873212"]}],"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":["61702444"],"award-info":[{"award-number":["61702444"]}],"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":["61572506"],"award-info":[{"award-number":["61572506"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"Chinese Postdoctoral Science Foundation","doi-asserted-by":"crossref","award":["2019M653804"],"award-info":[{"award-number":["2019M653804"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100013494","name":"West Light Foundation of The Chinese Academy of Sciences","doi-asserted-by":"publisher","award":["2018-XBQNXZ-B-008"],"award-info":[{"award-number":["2018-XBQNXZ-B-008"]}],"id":[{"id":"10.13039\/501100013494","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,7,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Emerging evidence indicates that circular RNA (circRNA) plays a crucial role in human disease. Using circRNA as biomarker gives rise to a new perspective regarding our diagnosing of diseases and understanding of disease pathogenesis. However, detection of circRNA\u2013disease associations by biological experiments alone is often blind, limited to small scale, high cost and time consuming. Therefore, there is an urgent need for reliable computational methods to rapidly infer the potential circRNA\u2013disease associations on a large scale and to provide the most promising candidates for biological experiments.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In this article, we propose an efficient computational method based on multi-source information combined with deep convolutional neural network (CNN) to predict circRNA\u2013disease associations. The method first fuses multi-source information including disease semantic similarity, disease Gaussian interaction profile kernel similarity and circRNA Gaussian interaction profile kernel similarity, and then extracts its hidden deep feature through the CNN and finally sends them to the extreme learning machine classifier for prediction. The 5-fold cross-validation results show that the proposed method achieves 87.21% prediction accuracy with 88.50% sensitivity at the area under the curve of 86.67% on the CIRCR2Disease dataset. In comparison with the state-of-the-art SVM classifier and other feature extraction methods on the same dataset, the proposed model achieves the best results. In addition, we also obtained experimental support for prediction results by searching published literature. As a result, 7 of the top 15 circRNA\u2013disease pairs with the highest scores were confirmed by literature. These results demonstrate that the proposed model is a suitable method for predicting circRNA\u2013disease associations and can provide reliable candidates for biological experiments.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The source code and datasets explored in this work are available at https:\/\/github.com\/look0012\/circRNA-Disease-association.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btz825","type":"journal-article","created":{"date-parts":[[2019,11,21]],"date-time":"2019-11-21T12:11:30Z","timestamp":1574338290000},"page":"4038-4046","source":"Crossref","is-referenced-by-count":128,"title":["<b>An efficient approach based on multi-sources information to predict circRNA<\/b>\u2013<b>disease associations using deep convolutional neural network<\/b>"],"prefix":"10.1093","volume":"36","author":[{"given":"Lei","family":"Wang","sequence":"first","affiliation":[{"name":"Xinjiang Technical Institute of Physics and Chemistry , Chinese Academy of Sciences, Urumqi 830011, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1266-2696","authenticated-orcid":false,"given":"Zhu-Hong","family":"You","sequence":"additional","affiliation":[{"name":"Xinjiang Technical Institute of Physics and Chemistry , Chinese Academy of Sciences, Urumqi 830011, China"}]},{"given":"Yu-An","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Computing , Hong Kong Polytechnic University, Hong Kong 999077, China"}]},{"given":"De-Shuang","family":"Huang","sequence":"additional","affiliation":[{"name":"Institute of Machine Learning and Systems Biology , School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China"}]},{"given":"Keith C C","family":"Chan","sequence":"additional","affiliation":[{"name":"Department of Computing , Hong Kong Polytechnic University, Hong Kong 999077, China"}]}],"member":"286","published-online":{"date-parts":[[2019,12,3]]},"reference":[{"key":"2023062300444363700_btz825-B1","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1016\/j.eswa.2016.09.041","article-title":"Multi-level hybrid support vector machine and extreme learning machine based on modified K-means for intrusion detection system","volume":"67","author":"Al-Yaseen","year":"2017","journal-title":"Expert Syst. Appl"},{"key":"2023062300444363700_btz825-B2","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1373\/clinchem.2014.230433","article-title":"The landscape of microRNA, Piwi-interacting RNA, and circular RNA in human saliva","volume":"61","author":"Bahn","year":"2015","journal-title":"Clin. Chem"},{"key":"2023062300444363700_btz825-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 Recogn"},{"key":"2023062300444363700_btz825-B4","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1111\/jnc.13752","article-title":"Characterization of circular RNAs landscape in multiple system atrophy brain","volume":"139","author":"Chen","year":"2016","journal-title":"J. Neurochem"},{"key":"2023062300444363700_btz825-B5","doi-asserted-by":"crossref","first-page":"4551","DOI":"10.1038\/onc.2017.89","article-title":"circRNA_100290 plays a role in oral cancer by functioning as a sponge of the miR-29 family","volume":"36","author":"Chen","year":"2017","journal-title":"Oncogene"},{"key":"2023062300444363700_btz825-B6","doi-asserted-by":"crossref","first-page":"3131","DOI":"10.1093\/nar\/gkr1009","article-title":"Transcriptome-wide discovery of circular RNAs in Archaea","volume":"40","author":"Danan","year":"2012","journal-title":"Nucleic Acids Res"},{"key":"2023062300444363700_btz825-B7","first-page":"6","article-title":"CircR2Disease: a manually curated database for experimentally supported circular RNAs associated with various diseases","volume":"1","author":"Fan","year":"2018","journal-title":"Database"},{"key":"2023062300444363700_btz825-B8","doi-asserted-by":"crossref","first-page":"1950","DOI":"10.7150\/ijbs.28260","article-title":"Prediction of CircRNA-disease associations using KATZ model based on heterogeneous networks","volume":"14","author":"Fan","year":"2018","journal-title":"Int. J. Biol. Sci"},{"key":"2023062300444363700_btz825-B9","doi-asserted-by":"crossref","first-page":"1080","DOI":"10.1039\/C4IB00136B","article-title":"An improved interolog mapping-based computational prediction of protein-protein interactions with increased network coverage","volume":"6","author":"Folador","year":"2014","journal-title":"Integr. Biol"},{"key":"2023062300444363700_btz825-B10","first-page":"1","article-title":"Ens-PPI: a novel ensemble classifier for predicting the interactions of proteins using autocovariance transformation from PSSM","volume":"8","author":"Gao","year":"2016","journal-title":"Biomed. Res. Int"},{"key":"2023062300444363700_btz825-B11","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1002\/prot.21097","article-title":"Predicting G-protein coupled receptors-G-protein coupling specificity based on autocross-covariance transform","volume":"65","author":"Guo","year":"2006","journal-title":"Proteins Struct. Funct. Bioinformatics"},{"key":"2023062300444363700_btz825-B12","doi-asserted-by":"crossref","first-page":"3025","DOI":"10.1093\/nar\/gkn159","article-title":"Using support vector machine combined with auto covariance to predict proteinprotein interactions from protein sequences","volume":"36","author":"Guo","year":"2008","journal-title":"Nucleic Acids Res"},{"key":"2023062300444363700_btz825-B13","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1038\/nature11993","article-title":"Natural RNA circles function as efficient microRNA sponges","volume":"495","author":"Hansen","year":"2013","journal-title":"Nature"},{"key":"2023062300444363700_btz825-B14","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","article-title":"Extreme learning machine: theory and applications","volume":"70","author":"Huang","year":"2006","journal-title":"Neurocomputing"},{"key":"2023062300444363700_btz825-B15","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1007\/s13042-011-0019-y","article-title":"Extreme learning machines: a survey","volume":"2","author":"Huang","year":"2011","journal-title":"Int. J. Mach. Learn. Cybern"},{"key":"2023062300444363700_btz825-B16","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1109\/TCYB.2015.2401973","article-title":"Graph embedded extreme learning machine","volume":"46","author":"Iosifidis","year":"2016","journal-title":"IEEE Trans. Cybern"},{"key":"2023062300444363700_btz825-B17","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1261\/rna.035667.112","article-title":"Circular RNAs are abundant, conserved, and associated with ALU repeats","volume":"19","author":"Jeck","year":"2013","journal-title":"RNA"},{"key":"2023062300444363700_btz825-B18","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","author":"Krizhevsky","year":"2012","journal-title":"International Conference on Neural Information Processing Systems"},{"key":"2023062300444363700_btz825-B19","first-page":"26, 4446\u20134456","article-title":"DeepFix: a fully convolutional neural network for predicting human eye fixations","author":"Kruthiventi","year":"2017","journal-title":"IEEE Trans. Image Process"},{"key":"2023062300444363700_btz825-B20","doi-asserted-by":"crossref","first-page":"3410","DOI":"10.3390\/ijms19113410","article-title":"PWCDA: path weighted method for predicting circRNA\u2013disease associations","volume":"19","author":"Lei","year":"2018","journal-title":"Int. J. Mol. Sci"},{"key":"2023062300444363700_btz825-B21","doi-asserted-by":"crossref","first-page":"3564","DOI":"10.1093\/hmg\/ddx243","article-title":"Circular RNA profiling reveals that circular RNAs from ANXA2 can be used as new biomarkers for multiple sclerosis","volume":"26","author":"Leire","year":"2017","journal-title":"Hum. Mol. Genet"},{"key":"2023062300444363700_btz825-B22","doi-asserted-by":"crossref","first-page":"e639","DOI":"10.7717\/peerj.639","article-title":"Associating disease-related genetic variants in intergenic regions to the genes they impact","volume":"2","author":"Macintyre","year":"2014","journal-title":"PeerJ"},{"key":"2023062300444363700_btz825-B23","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1038\/nature11928","article-title":"Circular RNAs are a large class of animal RNAs with regulatory potency","volume":"495","author":"Memczak","year":"2013","journal-title":"Nature"},{"key":"2023062300444363700_btz825-B24","doi-asserted-by":"crossref","first-page":"1671","DOI":"10.1007\/s00204-016-1837-1","article-title":"A novel regulatory network among LncRpa, CircRar1, MiR-671 and apoptotic genes promotes lead-induced neuronal cell apoptosis","volume":"91","author":"Nan","year":"2017","journal-title":"Arch. Toxicol"},{"key":"2023062300444363700_btz825-B25","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1016\/0092-8674(91)90244-S","article-title":"Scrambled exons","volume":"64","author":"Nigro","year":"1991","journal-title":"Cell"},{"key":"2023062300444363700_btz825-B26","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.neucom.2018.04.036","article-title":"Learning distributed representations of RNA sequences and its application for predicting RNA-protein binding sites with a convolutional neural network","volume":"305","author":"Pan","year":"2018","journal-title":"Neurocomputing"},{"key":"2023062300444363700_btz825-B27","doi-asserted-by":"crossref","first-page":"161","DOI":"10.3233\/CBM-150552","article-title":"Hsa_circ_0001649: a circular RNA and potential novel biomarker for hepatocellular carcinoma","volume":"16","author":"Qin","year":"2016","journal-title":"Cancer Biomark"},{"key":"2023062300444363700_btz825-B28","doi-asserted-by":"crossref","first-page":"73271","DOI":"10.18632\/oncotarget.19154","article-title":"An emerging function of circRNA-miRNAs-mRNA axis in human diseases","volume":"8","author":"Rong","year":"2017","journal-title":"Oncotarget"},{"key":"2023062300444363700_btz825-B29","doi-asserted-by":"crossref","first-page":"e1003777","DOI":"10.1371\/journal.pgen.1003777","article-title":"Cell-type specific features of circular RNA expression","volume":"9","author":"Salzman","year":"2013","journal-title":"PLoS Genet"},{"key":"2023062300444363700_btz825-B30","doi-asserted-by":"crossref","first-page":"3852","DOI":"10.1073\/pnas.73.11.3852","article-title":"Viroids are single-stranded covalently closed circular RNA molecules existing as highly base-paired rod-like structures","volume":"73","author":"Sanger","year":"1976","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"2023062300444363700_btz825-B31","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1126\/science.3287615","article-title":"Measuring the accuracy of diagnostic systems","volume":"240","author":"Swets","year":"1988","journal-title":"Science"},{"key":"2023062300444363700_btz825-B32","doi-asserted-by":"crossref","first-page":"3036","DOI":"10.1093\/bioinformatics\/btr500","article-title":"Gaussian interaction profile kernels for predicting drug\u2013target interaction","volume":"27","author":"van Laarhoven","year":"2011","journal-title":"Bioinformatics"},{"key":"2023062300444363700_btz825-B33","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"},{"key":"2023062300444363700_btz825-B34","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.jtbi.2017.01.003","article-title":"Advancing the prediction accuracy of protein-protein interactions by utilizing evolutionary information from position-specific scoring matrix and ensemble classifier","volume":"418","author":"Wang","year":"2017","journal-title":"J. Theoret. Biol"},{"key":"2023062300444363700_btz825-B35","doi-asserted-by":"crossref","first-page":"12874","DOI":"10.1038\/s41598-018-30694-1","article-title":"Using two-dimensional principal component analysis and rotation forest for prediction of protein-protein interactions","volume":"8","author":"Wang","year":"2018","journal-title":"Sci. Rep"},{"key":"2023062300444363700_btz825-B36","doi-asserted-by":"crossref","first-page":"445","DOI":"10.2174\/1389203718666161114111656","article-title":"RFDT: a rotation forest-based predictor for predicting drug-target interactions using drug structure and protein sequence information","volume":"19","author":"Wang","year":"2018","journal-title":"Curr. Prot. Peptide Sci"},{"key":"2023062300444363700_btz825-B37","doi-asserted-by":"crossref","first-page":"9848","DOI":"10.1038\/s41598-019-46369-4","article-title":"Predicting protein-protein interactions from matrix-based protein sequence using convolution neural network and feature-selective rotation forest","volume":"9","author":"Wang","year":"2019","journal-title":"Sci. Rep"},{"key":"2023062300444363700_btz825-B38","doi-asserted-by":"crossref","first-page":"e1006865","DOI":"10.1371\/journal.pcbi.1006865","article-title":"LMTRDA: using logistic model tree to predict MiRNA\u2013disease associations by fusing multi-source information of sequences and similarities","volume":"15","author":"Wang","year":"2019","journal-title":"PLoS Comput. Biol"},{"key":"2023062300444363700_btz825-B39","doi-asserted-by":"crossref","first-page":"870","DOI":"10.1016\/j.molcel.2015.03.027","article-title":"Circular RNAs in the mammalian brain are highly abundant, conserved, and dynamically expressed","volume":"58","author":"Wolf","year":"2015","journal-title":"Mol. Cell"},{"key":"2023062300444363700_btz825-B40","doi-asserted-by":"crossref","first-page":"S9","DOI":"10.1186\/1752-0509-7-S3-S9","article-title":"A genome-wide MeSH-based literature mining system predicts implicit gene-to-gene relationships and networks","volume":"7","author":"Xiang","year":"2013","journal-title":"BMC Syst. Biol"},{"key":"2023062300444363700_btz825-B41","doi-asserted-by":"crossref","first-page":"e70204","DOI":"10.1371\/journal.pone.0070204","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"},{"key":"2023062300444363700_btz825-B42","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1186\/s12859-018-2522-6","article-title":"DWNN-RLS: regularized least squares method for predicting circRNA\u2013disease associations","volume":"19","author":"Yan","year":"2018","journal-title":"BMC Bioinformatics"},{"key":"2023062300444363700_btz825-B43","first-page":"97","author":"Yu","year":"2017"},{"key":"2023062300444363700_btz825-B44","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1186\/s12967-019-2009-x","article-title":"MLMDA: a machine learning approach to predict and validate microRNA\u2013disease associations by integrating of heterogenous information sources","volume":"17","author":"Zheng","year":"2019","journal-title":"J. Transl. Med"},{"key":"2023062300444363700_btz825-B45","doi-asserted-by":"crossref","first-page":"769","DOI":"10.1016\/j.bbrc.2017.04.044","article-title":"A novel identified circular RNA, circRNA_010567, promotes myocardial fibrosis via suppressing miR-141 by targeting TGF-\u03b21","volume":"487","author":"Zhou","year":"2017","journal-title":"Biochem. Biophys. Res. Commun"},{"key":"2023062300444363700_btz825-B46","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.cell.2016.02.011","article-title":"Gut microbial metabolite TMAO enhances platelet hyperreactivity and thrombosis risk","volume":"165","author":"Zhu","year":"2016","journal-title":"Cell"},{"key":"2023062300444363700_btz825-B47","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1093\/clinchem\/39.4.561","article-title":"Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine","volume":"39","author":"Zweig","year":"1993","journal-title":"Clin. Chem"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btz825\/33158228\/btz825.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/36\/13\/4038\/50671421\/bioinformatics_36_13_4038.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/36\/13\/4038\/50671421\/bioinformatics_36_13_4038.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,24]],"date-time":"2023-06-24T18:36:51Z","timestamp":1687631811000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/36\/13\/4038\/5651016"}},"subtitle":[],"editor":[{"given":"Jan","family":"Gorodkin","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2019,12,3]]},"references-count":47,"journal-issue":{"issue":"13","published-print":{"date-parts":[[2020,7,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btz825","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2020,7]]},"published":{"date-parts":[[2019,12,3]]}}}