{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T17:51:30Z","timestamp":1740160290651,"version":"3.37.3"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,9,17]],"date-time":"2024-09-17T00:00:00Z","timestamp":1726531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,9,17]],"date-time":"2024-09-17T00:00:00Z","timestamp":1726531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Fundamental Research Funds for the Central Universities, China University of Geosciences","award":["G1323523061","G1323523041","G1323523061","G1323523041"],"award-info":[{"award-number":["G1323523061","G1323523041","G1323523061","G1323523041"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["12371506","12305054","12172340"],"award-info":[{"award-number":["12371506","12305054","12172340"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2025,3]]},"DOI":"10.1007\/s13042-024-02375-1","type":"journal-article","created":{"date-parts":[[2024,9,18]],"date-time":"2024-09-18T13:07:19Z","timestamp":1726664839000},"page":"2023-2039","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["CHNSCDA: circRNA-disease association prediction based on strongly correlated heterogeneous neighbor sampling"],"prefix":"10.1007","volume":"16","author":[{"given":"Yuanyuan","family":"Lin","sequence":"first","affiliation":[]},{"given":"Nianrui","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Jiangyan","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Fangqin","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Zhouchao","family":"Wei","sequence":"additional","affiliation":[]},{"given":"Ming","family":"Yi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,9,17]]},"reference":[{"key":"2375_CR1","doi-asserted-by":"publisher","first-page":"7224","DOI":"10.1007\/s12035-016-0213-8","volume":"54","author":"L Kumar","year":"2017","unstructured":"Kumar L, Shamsuzzama H et al (2017) Circular RNAs: the emerging class of non-coding RNAs and their potential role in human neurodegenerative diseases. Mol Neurobiol 54:7224\u20137234. https:\/\/doi.org\/10.1007\/s12035-016-0213-8","journal-title":"Mol Neurobiol"},{"key":"2375_CR2","doi-asserted-by":"publisher","first-page":"73271","DOI":"10.18632\/oncotarget.19154","volume":"8","author":"D Rong","year":"2017","unstructured":"Rong D, Sun H, Li Z et al (2017) An emerging function of circRNA-miRNAs-mRNA axis in human diseases. Oncotarget 8:73271\u201373281. https:\/\/doi.org\/10.18632\/oncotarget.19154","journal-title":"Oncotarget"},{"key":"2375_CR3","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0030733","volume":"7","author":"J Salzman","year":"2012","unstructured":"Salzman J, Gawad C, Wang PL et al (2012) Circular RNAs are the predominant transcript isoform from hundreds of human genes in diverse cell types. PLoS ONE 7:e30733. https:\/\/doi.org\/10.1371\/journal.pone.0030733","journal-title":"PLoS ONE"},{"key":"2375_CR4","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1038\/nature11928","volume":"495","author":"S Memczak","year":"2013","unstructured":"Memczak S, Jens M, Elefsinioti A et al (2013) Circular RNAs are a large class of animal RNAs with regulatory potency. Nature 495:333\u2013338. https:\/\/doi.org\/10.1038\/nature11928","journal-title":"Nature"},{"key":"2375_CR5","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1038\/nsmb.2959","volume":"22","author":"Z Li","year":"2015","unstructured":"Li Z, Huang C, Bao C et al (2015) Exon-intron circular RNAs regulate transcription in the nucleus. Nat Struct Mol Biol 22:256\u2013264. https:\/\/doi.org\/10.1038\/nsmb.2959","journal-title":"Nat Struct Mol Biol"},{"key":"2375_CR6","doi-asserted-by":"publisher","first-page":"384","DOI":"10.1038\/nature11993","volume":"495","author":"TB Hansen","year":"2013","unstructured":"Hansen TB, Jensen TI, Clausen BH et al (2013) Natural RNA circles function as efficient microRNA sponges. Nature 495:384\u2013388. https:\/\/doi.org\/10.1038\/nature11993","journal-title":"Nature"},{"key":"2375_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12943-021-01417-4","volume":"20","author":"L He","year":"2021","unstructured":"He L, Man C, Xiang S et al (2021) Circular RNAs\u2019 cap-independent translation protein and its roles in carcinomas. Mol Cancer 20:1\u201311. https:\/\/doi.org\/10.1186\/s12943-021-01417-4","journal-title":"Mol Cancer"},{"key":"2375_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12943-019-1002-6","volume":"18","author":"S Min","year":"2019","unstructured":"Min S, Yu X, Jun M et al (2019) Circular RNAs in cancer: emerging functions in hallmarks, stemness, resistance and roles as potential biomarkers. Mol Cancer 18:1\u201317. https:\/\/doi.org\/10.1186\/s12943-019-1002-6","journal-title":"Mol Cancer"},{"key":"2375_CR9","doi-asserted-by":"publisher","first-page":"850","DOI":"10.3389\/fcell.2020.00850","volume":"8","author":"Z Chen","year":"2020","unstructured":"Chen Z, Jiang H, Yi Y (2020) CircRNA is a rising star in researches of ocular diseases. Front Cell Dev Biol 8:850. https:\/\/doi.org\/10.3389\/fcell.2020.00850","journal-title":"Front Cell Dev Biol"},{"key":"2375_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12943-017-0663-2","volume":"16","author":"S Meng","year":"2017","unstructured":"Meng S, Zhou H, Feng Z et al (2017) CircRNA: functions and properties of a novel potential biomarker for cancer. Mol Cancer 16:1\u20138. https:\/\/doi.org\/10.1186\/s12943-017-0663-2","journal-title":"Mol Cancer"},{"key":"2375_CR11","doi-asserted-by":"publisher","first-page":"457","DOI":"10.19540\/j.cnki.cjcmm.20171106.012","volume":"43","author":"J Gao","year":"2018","unstructured":"Gao J, Chen G, He H et al (2018) CircRNA as a new field in human disease research. Zhongguo Zhong Yao Za Zhi 43:457\u2013462. https:\/\/doi.org\/10.19540\/j.cnki.cjcmm.20171106.012","journal-title":"Zhongguo Zhong Yao Za Zhi"},{"key":"2375_CR12","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1016\/j.cell.2016.03.020","volume":"165","author":"J Guarnerio","year":"2016","unstructured":"Guarnerio J, Bezzi M, Jeong J et al (2016) Oncogenic role of fusioncircRNAs derived from cancer-associated chromosomal translocations. Cell 165:289\u2013302. https:\/\/doi.org\/10.1016\/j.cell.2016.03.020","journal-title":"Cell"},{"key":"2375_CR13","doi-asserted-by":"publisher","DOI":"10.1093\/database\/baaa085","volume":"2020","author":"W Lan","year":"2020","unstructured":"Lan W, Zhu M, Chen Q et al (2020) Circr2cancer: a manually curated database of associations between circRNAs and cancers. Database 2020:baaa085. https:\/\/doi.org\/10.1093\/database\/baaa085","journal-title":"Database"},{"key":"2375_CR14","doi-asserted-by":"publisher","first-page":"1774","DOI":"10.1109\/TCBB.2016.2586190","volume":"15","author":"W Lan","year":"2016","unstructured":"Lan W, Wang J, Li M et al (2016) Predicting microRNA-disease associations based on improved microRNA and disease similarities. IEEE\/ACM Trans Comput Biol Bioinf 15:1774\u20131782. https:\/\/doi.org\/10.1109\/TCBB.2016.2586190","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"key":"2375_CR15","doi-asserted-by":"publisher","first-page":"78847","DOI":"10.1109\/ACCESS.2021.3084148","volume":"9","author":"H Jihwan","year":"2021","unstructured":"Jihwan H, Sanghyun P (2021) MLMD: metric learning for predicting miRNA-disease associations. IEEE Access 9:78847\u201378858. https:\/\/doi.org\/10.1109\/ACCESS.2021.3084148","journal-title":"IEEE Access"},{"key":"2375_CR16","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1186\/s12864-024-09998-2","volume":"25","author":"L Guang","year":"2024","unstructured":"Guang L, Pei B, Cheng L et al (2024) Node-adaptive graph transformer with structural encoding for accurate and robust lncRNA-disease association prediction. BMC Genomics 25:73. https:\/\/doi.org\/10.1186\/s12864-024-09998-2","journal-title":"BMC Genomics"},{"key":"2375_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.108062","volume":"170","author":"L Guang","year":"2024","unstructured":"Guang L, Pei B, Jiao C et al (2024) Identifying virulence factors using graph transformer autoencoder with ESMFold-predicted structures. Comput Biol Med 170:108062. https:\/\/doi.org\/10.1016\/j.compbiomed.2024.108062","journal-title":"Comput Biol Med"},{"key":"2375_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2019.103358","volume":"102","author":"H Jihwan","year":"2020","unstructured":"Jihwan H, Chihyun P, Chanyoung P et al (2020) IMIPMF: inferring miRNA-disease interactions using probabilistic matrix factorization. J Biomed Inform 102:103358. https:\/\/doi.org\/10.1016\/j.jbi.2019.103358","journal-title":"J Biomed Inform"},{"key":"2375_CR19","doi-asserted-by":"publisher","first-page":"1257","DOI":"10.1109\/TCBB.2022.3191972","volume":"20","author":"H Jihwan","year":"2023","unstructured":"Jihwan H, Sanghyun P (2023) NCMD: node2vec-based neural collaborative filtering for predicting miRNA-disease association. IEEE\/ACM Trans Comput Biol Bioinf 20:1257\u20131268. https:\/\/doi.org\/10.1109\/TCBB.2022.3191972","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"key":"2375_CR20","doi-asserted-by":"publisher","first-page":"885","DOI":"10.3390\/jpm12060885","volume":"12","author":"H Jihwan","year":"2022","unstructured":"Jihwan H (2022) MDMF: predicting miRNA-disease association based on matrix factorization with disease similarity constraint. J Pers Med 12:885. https:\/\/doi.org\/10.3390\/jpm12060885","journal-title":"J Pers Med"},{"key":"2375_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110295","author":"J Ha","year":"2023","unstructured":"Ha J (2023) SMAP: similarity-based matrix factorization framework for inferring miRNA-disease association. Knowl-Based Syst. https:\/\/doi.org\/10.1016\/j.knosys.2023.110295","journal-title":"Knowl-Based Syst"},{"key":"2375_CR22","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1186\/s12859-021-04073-z","volume":"22","author":"S Zhuang","year":"2021","unstructured":"Zhuang S, Han Z, Chen J et al (2021) A representation learning model based on variational inference and graph autoencoder for predicting lncRNA-disease associations. BMC Bioinform 22:136. https:\/\/doi.org\/10.1186\/s12859-021-04073-z","journal-title":"BMC Bioinform"},{"key":"2375_CR23","doi-asserted-by":"publisher","first-page":"64","DOI":"10.3390\/biom12010064","volume":"12","author":"J Chen","year":"2022","unstructured":"Chen J, Zhuang S, Ken L et al (2022) Predicting miRNA-disease association based on neural inductive matrix completion with graph autoencoders and self-attention mechanism. Biomolecules 12:64. https:\/\/doi.org\/10.3390\/biom12010064","journal-title":"Biomolecules"},{"key":"2375_CR24","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbab286","volume":"22","author":"W Chun","year":"2021","unstructured":"Chun W, Chen H, Qi Z et al (2021) Circular RNAs and complex diseases: from experimental results to computational models. Brief Bioinform 22:bbab286. https:\/\/doi.org\/10.1093\/bib\/bbab286","journal-title":"Brief Bioinform"},{"key":"2375_CR25","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1005420","volume":"13","author":"Z Xiang","year":"2017","unstructured":"Xiang Z, Wei L, Mao G et al (2017) A comprehensive overview and evaluation of circular RNA detection tools. PLoS Comput Biol 13:e1005420. https:\/\/doi.org\/10.1371\/journal.pcbi.1005420","journal-title":"PLoS Comput Biol"},{"key":"2375_CR26","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1016\/j.csbj.2020.03.028","volume":"18","author":"M Niu","year":"2020","unstructured":"Niu M, Zhang J, Li Y et al (2020) CirRNAPL: a web server for the identification of circRNA based on extreme learning machine. Comput Struct Biotechnol 18:834\u2013842. https:\/\/doi.org\/10.1016\/j.csbj.2020.03.028","journal-title":"Comput Struct Biotechnol"},{"key":"2375_CR27","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2021.665233","volume":"12","author":"S Jiao","year":"2021","unstructured":"Jiao S, Wu S, Huang S et al (2021) Advances in the identification of circular RNAs and research into circRNAs in human diseases. Front Genet 12:665233. https:\/\/doi.org\/10.3389\/fgene.2021.665233","journal-title":"Front Genet"},{"key":"2375_CR28","doi-asserted-by":"publisher","first-page":"2246","DOI":"10.1093\/bioinformatics\/btac079","volume":"38","author":"N Meng","year":"2022","unstructured":"Meng N, Quan Z, Chun W (2022) GMNN2CD: Identification of circRNA\u2013disease associations based on variational inference and graph Markov neural networks. Bioinformatics 38:2246\u20132253. https:\/\/doi.org\/10.1093\/bioinformatics\/btac079","journal-title":"Bioinformatics"},{"key":"2375_CR29","doi-asserted-by":"publisher","first-page":"1425","DOI":"10.1093\/bib\/bbz080","volume":"21","author":"X Zeng","year":"2020","unstructured":"Zeng X, Zhong Y, Lin W et al (2020) Predicting disease-associated circular RNAs using deep forests combined with positive-unlabeled learning methods. Brief Bioinform 21:1425\u20131436. https:\/\/doi.org\/10.1093\/bib\/bbz080","journal-title":"Brief Bioinform"},{"key":"2375_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105322","volume":"143","author":"C Yao","year":"2022","unstructured":"Yao C, Yan W, Yi D et al (2022) RGCNCDA: relational graph convolutional network improves circRNA-disease association prediction by incorporating microRNAs. Comput Biol Med 143:105322. https:\/\/doi.org\/10.1016\/j.compbiomed.2022.105322","journal-title":"Comput Biol Med"},{"key":"2375_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiolchem.2022.107722","volume":"99","author":"L Guang","year":"2022","unstructured":"Guang L, Ya L, Jia L et al (2022) GGAECDA: predicting circRNA-disease associations using graph autoencoder based on graph representation learning. Comput Biol Chem 99:107722. https:\/\/doi.org\/10.1016\/j.compbiolchem.2022.107722","journal-title":"Comput Biol Chem"},{"key":"2375_CR32","doi-asserted-by":"publisher","first-page":"5075","DOI":"10.1109\/JBHI.2022.3199462","volume":"26","author":"W Lei","year":"2022","unstructured":"Lei W, Leon W, Zhu Y et al (2022) NSECDA: natural semantic enhancement for CircRNA-disease association prediction. IEEE J Biomed Health Inform 26:5075\u20135084. https:\/\/doi.org\/10.1109\/JBHI.2022.3199462","journal-title":"IEEE J Biomed Health Inform"},{"key":"2375_CR33","doi-asserted-by":"publisher","first-page":"83474","DOI":"10.1109\/ACCESS.2019.2920942","volume":"7","author":"Z Wen","year":"2019","unstructured":"Wen Z, Chen Y, Xiao W et al (2019) Predicting circRNA-disease associations through linear neighborhood label propagation method. IEEE Access 7:83474\u201383483. https:\/\/doi.org\/10.1109\/ACCESS.2019.2920942","journal-title":"IEEE Access"},{"key":"2375_CR34","doi-asserted-by":"publisher","first-page":"1335","DOI":"10.1016\/j.ygeno.2019.08.001","volume":"112","author":"G Erxia","year":"2020","unstructured":"Erxia G, Ying Y, Ming G et al (2020) Predicting human disease-associated circRNAs based on locality-constrained linear coding. Genomics 112:1335\u20131342. https:\/\/doi.org\/10.1016\/j.ygeno.2019.08.001","journal-title":"Genomics"},{"key":"2375_CR35","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbac155","volume":"23","author":"L Peng","year":"2022","unstructured":"Peng L, Cheng Y, Li H et al (2022) RNMFLP: Predicting circRNA\u2013disease associations based on robust nonnegative matrix factorization and label propagation. Brief Bioinform 23:bbac155. https:\/\/doi.org\/10.1093\/bib\/bbac155","journal-title":"Brief Bioinform"},{"key":"2375_CR36","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1186\/s12859-018-2522-6","volume":"19","author":"Y Cheng","year":"2018","unstructured":"Cheng Y, Jian W (2018) DWNN-RLS: regularized least squares method for predicting circRNA-disease associations. BMC Bioinform 19:73\u201381. https:\/\/doi.org\/10.1186\/s12859-018-2522-6","journal-title":"BMC Bioinform"},{"key":"2375_CR37","doi-asserted-by":"publisher","unstructured":"Huseyin V, Mehmet K, Reda A (2019) A model based on random walk with restart to predict circRNA-disease associations on heterogeneous network. In: IEEE\/ACM international conference on advances in social networks analysis and mining, pp 929\u2013932. https:\/\/doi.org\/10.1145\/3341161.3343514","DOI":"10.1145\/3341161.3343514"},{"key":"2375_CR38","doi-asserted-by":"publisher","first-page":"3410","DOI":"10.3390\/ijms19113410","volume":"19","author":"L Xiu","year":"2018","unstructured":"Xiu L, Zeng F, Luo C et al (2018) PWCDA: pathweighted method for predicting circRNA-disease associations. Int J Biol Sci 19:3410. https:\/\/doi.org\/10.3390\/ijms19113410","journal-title":"Int J Biol Sci"},{"key":"2375_CR39","doi-asserted-by":"publisher","first-page":"1950","DOI":"10.7150\/ijbs.28260","volume":"14","author":"F Chun","year":"2018","unstructured":"Chun F, Xiu L, Fang W (2018) Prediction of circRNA-disease associations using KATZ model based on heterogeneous networks. Int J Biol Sci 14:1950\u20131959. https:\/\/doi.org\/10.7150\/ijbs.28260","journal-title":"Int J Biol Sci"},{"key":"2375_CR40","doi-asserted-by":"publisher","DOI":"10.1093\/bfgp\/elad042","author":"G Yuwei","year":"2023","unstructured":"Yuwei G, Ming Y (2023) THGNCDA: circRNA-disease association prediction based on triple heterogeneous graph network. Brief Funct Genomics. https:\/\/doi.org\/10.1093\/bfgp\/elad042","journal-title":"Brief Funct Genomics"},{"key":"2375_CR41","doi-asserted-by":"publisher","first-page":"3530","DOI":"10.1109\/TCBB.2021.3111607","volume":"19","author":"L Wei","year":"2022","unstructured":"Wei L, Yi D, Qing C et al (2022) IGNSCDA: predicting circRNA-disease associations based on improved graph convolutional network and negative sampling. IEEE\/ACM Trans Comput Biol Bioinf 19:3530\u20133538. https:\/\/doi.org\/10.1109\/TCBB.2021.3111607","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"key":"2375_CR42","doi-asserted-by":"publisher","first-page":"891","DOI":"10.1109\/JBHI.2020.2999638","volume":"25","author":"L Cheng","year":"2022","unstructured":"Cheng L, Min Z, Fu Z et al (2022) Deep matrix factorization improves prediction of human circRNA-disease associations. IEEE J Biomed Health Inform 25:891\u2013899. https:\/\/doi.org\/10.1109\/JBHI.2020.2999638","journal-title":"IEEE J Biomed Health Inform"},{"key":"2375_CR43","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1706.03762","author":"V Ashish","year":"2017","unstructured":"Ashish V, Noam S, Niki P et al (2017) Attention is all you need. In: 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA, pp 5998\u20136008. arXiv:1706.03762","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"2375_CR44","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1710.10903","author":"V Peter","year":"2017","unstructured":"Peter V, Guillem C, Arantxa C et al (2017) Graph attention networks. Machine learning, vol 1050. arXiv:1710.10903","journal-title":"Mach Learn"},{"key":"2375_CR45","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2022.829937","volume":"13","author":"L Guang","year":"2022","unstructured":"Guang L, Dian W, Yue Z et al (2022) Using graph attention network and graph convolutional network to explore human circRNA-disease associations based on multi-source data. Front Genet 13:829937. https:\/\/doi.org\/10.3389\/fgene.2022.829937","journal-title":"Front Genet"},{"key":"2375_CR46","doi-asserted-by":"publisher","first-page":"3072","DOI":"10.1109\/JBHI.2023.3260863","volume":"27","author":"L Peng","year":"2023","unstructured":"Peng L, Yang C, Yifan et al (2023) Predicting CircRNA-disease associations via feature convolution learning with heterogeneous graph attention network. IEEE J Biomed Health Inform 27:3072\u20133082. https:\/\/doi.org\/10.1109\/JBHI.2023.3260863","journal-title":"IEEE J Biomed Health Inform"},{"key":"2375_CR47","doi-asserted-by":"publisher","DOI":"10.1093\/database\/bay044","volume":"2018","author":"C Fan","year":"2018","unstructured":"Fan C, Xiu L, Zeng F et al (2018) CircR2disease: a manually curated database for experimentally supported circular RNAs associated with various diseases. Database 2018:bay044. https:\/\/doi.org\/10.1093\/database\/bay044","journal-title":"Database"},{"key":"2375_CR48","doi-asserted-by":"publisher","DOI":"10.1093\/database\/baaa085","volume":"2020","author":"L Wei","year":"2020","unstructured":"Wei L, Ming Z, Qing C et al (2020) Circr2cancer: a manually curated database of associations between circRNAs and cancers. Database 2020:baaa085. https:\/\/doi.org\/10.1093\/database\/baaa085","journal-title":"Database"},{"key":"2375_CR49","doi-asserted-by":"publisher","first-page":"11018","DOI":"10.1038\/s41598-018-29360-3","volume":"8","author":"Y Dong","year":"2018","unstructured":"Dong Y, Lei Z, Meng Z et al (2018) Circ2disease: a manually curated database of experimentally validated circRNAs in human disease. Sci Rep 8:11018. https:\/\/doi.org\/10.1038\/s41598-018-29360-3","journal-title":"Sci Rep"},{"key":"2375_CR50","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41419-018-0503-3","volume":"9","author":"Z Zheng","year":"2018","unstructured":"Zheng Z, Kuan W, Fan W et al (2018) circRNA disease: a manually curated database of experimentally supported circRNA-disease associations. Cell Death Dis 9:1\u20132. https:\/\/doi.org\/10.1038\/s41419-018-0503-3","journal-title":"Cell Death Dis"},{"key":"2375_CR51","doi-asserted-by":"publisher","first-page":"1071","DOI":"10.1093\/nar\/gku1011","volume":"43","author":"AK Warren","year":"2015","unstructured":"Warren AK, Cesar A, Victor F et al (2015) Disease Ontology 2015 update: an expanded and updated database of human diseases for linking biomedical knowledge through disease data. Nucleic Acids Res 43:1071\u20131078. https:\/\/doi.org\/10.1093\/nar\/gku1011","journal-title":"Nucleic Acids Res"},{"key":"2375_CR52","doi-asserted-by":"publisher","first-page":"1274","DOI":"10.1093\/bioinformatics\/btm087","volume":"23","author":"ZW James","year":"2007","unstructured":"James ZW, Zhi D, Rapeeporrn P et al (2007) A new method to measure the semantic similarity of go terms. Bioinformatics 23:1274\u20131281. https:\/\/doi.org\/10.1093\/bioinformatics\/btm087","journal-title":"Bioinformatics"},{"key":"2375_CR53","doi-asserted-by":"publisher","first-page":"608","DOI":"10.1093\/bioinformatics\/btu684","volume":"31","author":"Y Guang","year":"2015","unstructured":"Guang Y, Li W, Guang Y et al (2015) Dose: an r\/bioconductor package for disease ontology semantic and enrichment analysis. Bioinformatics 31:608\u2013609. https:\/\/doi.org\/10.1093\/bioinformatics\/btu684","journal-title":"Bioinformatics"},{"key":"2375_CR54","doi-asserted-by":"publisher","first-page":"1666","DOI":"10.1261\/rna.043687.113","volume":"20","author":"G Peter","year":"2014","unstructured":"Peter G, Panagiotis P, Nikolaus R (2014) circbase: a database for circular RNAs. RNA 20:1666\u20131670. https:\/\/doi.org\/10.1261\/rna.043687.113","journal-title":"RNA"},{"key":"2375_CR55","first-page":"707","volume":"10","author":"VL Levenshtein","year":"1966","unstructured":"Levenshtein VL (1966) Binary codes capable of correcting deletions, insertions, and reversals. Sov Phys Doklady 10:707\u2013710","journal-title":"Sov Phys Doklady"},{"key":"2375_CR56","doi-asserted-by":"publisher","first-page":"11338","DOI":"10.1038\/srep11338","volume":"5","author":"C Xing","year":"2015","unstructured":"Xing C, Cheng Y, Cai L et al (2015) Constructing lncRNA functional similarity network based on lncRNA-disease associations and disease semantic similarity. Sci Rep 5:11338. https:\/\/doi.org\/10.1038\/srep11338","journal-title":"Sci Rep"},{"key":"2375_CR57","doi-asserted-by":"publisher","first-page":"952","DOI":"10.1080\/15476286.2017.1312226","volume":"14","author":"C Xing","year":"2017","unstructured":"Xing C, Qiao W, Gui Y (2017) RKNNMDA: ranking-based KNN for miRNA-disease association prediction. RNA Biol 14:952\u2013962. https:\/\/doi.org\/10.1080\/15476286.2017.1312226","journal-title":"RNA Biol"},{"key":"2375_CR58","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0070204","volume":"8","author":"X Ping","year":"2013","unstructured":"Ping X, Ke H, Mao G et al (2013) Prediction of microRNAs associated with human diseases based on weighted k most similar neighbors. PLoS ONE 8:e70204. https:\/\/doi.org\/10.1371\/journal.pone.0070204","journal-title":"PLoS ONE"},{"key":"2375_CR59","doi-asserted-by":"publisher","unstructured":"Shaked B, Uri A, Eran Y (2021) How attentive are graph attention networks?. arXiv e-prints http:\/\/arxiv.org\/abs\/2105.14491. https:\/\/doi.org\/10.48550\/arXiv.2105.14491","DOI":"10.48550\/arXiv.2105.14491"},{"key":"2375_CR60","doi-asserted-by":"publisher","unstructured":"Xiang H, Li L, Han Z et al (2017) Neural collaborative filtering. In: Proc. 26th int. conf. world wide web, pp 173\u2013182. https:\/\/doi.org\/10.1145\/3038912.3052569","DOI":"10.1145\/3038912.3052569"},{"key":"2375_CR61","doi-asserted-by":"publisher","first-page":"281","DOI":"10.3390\/a1410028","volume":"14","author":"T Zhen","year":"2021","unstructured":"Zhen T, La P, Pu Y et al (2021) Information fusion-based deep neural attentive matrix factorization recommendation. Algorithms 14:281. https:\/\/doi.org\/10.3390\/a1410028","journal-title":"Algorithms"},{"key":"2375_CR62","doi-asserted-by":"publisher","first-page":"2044","DOI":"10.1016\/j.ins.2009.12.010","volume":"180","author":"G Salvador","year":"2010","unstructured":"Salvador G, Alberto F, Julian L et al (2010) Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: experimental analysis of power. Inf Sci 180:2044\u20132064. https:\/\/doi.org\/10.1016\/j.ins.2009.12.010","journal-title":"Inf Sci"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02375-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-024-02375-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02375-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T07:36:11Z","timestamp":1739950571000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-024-02375-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,17]]},"references-count":62,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,3]]}},"alternative-id":["2375"],"URL":"https:\/\/doi.org\/10.1007\/s13042-024-02375-1","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"type":"print","value":"1868-8071"},{"type":"electronic","value":"1868-808X"}],"subject":[],"published":{"date-parts":[[2024,9,17]]},"assertion":[{"value":"18 November 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 August 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 September 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 no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}