{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T12:03:58Z","timestamp":1754481838575,"version":"3.37.3"},"reference-count":66,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,9,27]],"date-time":"2022-09-27T00:00:00Z","timestamp":1664236800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,9,27]],"date-time":"2022-09-27T00:00:00Z","timestamp":1664236800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001824","name":"Grantov\u00e1 Agentura \u010cesk\u00e9 Republiky","doi-asserted-by":"publisher","award":["20-19162S","20-19162S"],"award-info":[{"award-number":["20-19162S","20-19162S"]}],"id":[{"id":"10.13039\/501100001824","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001824","name":"Grantov\u00e1 Agentura \u010cesk\u00e9 Republiky","doi-asserted-by":"publisher","award":["20-19162S"],"award-info":[{"award-number":["20-19162S"]}],"id":[{"id":"10.13039\/501100001824","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000780","name":"European Commission","doi-asserted-by":"publisher","award":["CZ.02.1.01\/0.0\/0.0\/16_019\/0000765","CZ.02.1.01\/0.0\/0.0\/16_019\/0000765"],"award-info":[{"award-number":["CZ.02.1.01\/0.0\/0.0\/16_019\/0000765","CZ.02.1.01\/0.0\/0.0\/16_019\/0000765"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Recent research has already shown that circular RNAs (circRNAs) are functional in gene expression regulation and potentially related to diseases. Due to their stability, circRNAs can also be used as biomarkers for diagnosis. However, the function of most circRNAs remains unknown, and it is expensive and time-consuming to discover it through biological experiments. In this paper, we predict circRNA annotations from the knowledge of their interaction with miRNAs and subsequent miRNA\u2013mRNA interactions. First, we construct an interaction network for a target circRNA and secondly spread the information from the network nodes with the known function to the root circRNA node. This idea itself is not new; our main contribution lies in proposing an efficient and exact deterministic procedure based on the principle of probability-generating functions to calculate the<jats:italic>p<\/jats:italic>-value of association test between a circRNA and an annotation term. We show that our publicly available algorithm is both more effective and efficient than the commonly used Monte-Carlo sampling approach that may suffer from difficult quantification of sampling convergence and subsequent sampling inefficiency. We experimentally demonstrate that the new approach is two orders of magnitude faster than the Monte-Carlo sampling, which makes summary annotation of large circRNA files feasible; this includes their reannotation after periodical interaction network updates, for example. We provide a summary annotation of a current circRNA database as one of our outputs. The proposed algorithm could be generalized towards other types of RNA in way that is straightforward.<\/jats:p>","DOI":"10.1186\/s12859-022-04957-8","type":"journal-article","created":{"date-parts":[[2022,9,27]],"date-time":"2022-09-27T10:03:17Z","timestamp":1664272997000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["circGPA: circRNA functional annotation based on probability-generating functions"],"prefix":"10.1186","volume":"23","author":[{"given":"Petr","family":"Ry\u0161av\u00fd","sequence":"first","affiliation":[]},{"given":"Ji\u0159\u00ed","family":"Kl\u00e9ma","sequence":"additional","affiliation":[]},{"given":"Michaela Dost\u00e1lov\u00e1","family":"Merkerov\u00e1","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,27]]},"reference":[{"issue":"4","key":"4957_CR1","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1016\/j.gpb.2018.08.001","volume":"16","author":"R Dong","year":"2018","unstructured":"Dong R, Ma X-K, Li G-W, Yang L. CIRCpedia v2: an updated database for comprehensive circular RNA annotation and expression comparison. Genom Proteom Bioinform. 2018;16(4):226\u201333. https:\/\/doi.org\/10.1016\/j.gpb.2018.08.001 (Bioinformatics Commons (I)).","journal-title":"Genom Proteom Bioinform"},{"issue":"5","key":"4957_CR2","doi-asserted-by":"publisher","first-page":"468","DOI":"10.1038\/s41419-021-03743-3","volume":"12","author":"L Verduci","year":"2021","unstructured":"Verduci L, Tarcitano E, Strano S, Yarden Y, Blandino G. CircRNAs: role in human diseases and potential use as biomarkers. Cell Death Dis. 2021;12(5):468. https:\/\/doi.org\/10.1038\/s41419-021-03743-3.","journal-title":"Cell Death Dis"},{"key":"4957_CR3","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/j.pharmthera.2018.01.010","volume":"187","author":"B Han","year":"2018","unstructured":"Han B, Chao J, Yao H. Circular RNA and its mechanisms in disease: from the bench to the clinic. Pharmacol Ther. 2018;187:31\u201344. https:\/\/doi.org\/10.1016\/j.pharmthera.2018.01.010.","journal-title":"Pharmacol Ther"},{"key":"4957_CR4","doi-asserted-by":"publisher","unstructured":"Wang C-C, Han C-D, Zhao Q, Chen X. Circular RNAs and complex diseases: from experimental results to computational models. Briefings in Bioinformatics (2021). https:\/\/doi.org\/10.1093\/bib\/bbab286. bbab286. https:\/\/academic.oup.com\/bib\/advance-article-pdf\/doi\/10.1093\/bib\/bbab286\/39715891\/bbab286.pdf","DOI":"10.1093\/bib\/bbab286"},{"issue":"1","key":"4957_CR5","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1186\/s12943-017-0663-2","volume":"16","author":"S Meng","year":"2017","unstructured":"Meng S, Zhou H, Feng Z, Xu Z, Tang Y, Li P, Wu M. CircRNA: functions and properties of a novel potential biomarker for cancer. Mol Cancer. 2017;16(1):94. https:\/\/doi.org\/10.1186\/s12943-017-0663-2.","journal-title":"Mol Cancer"},{"key":"4957_CR6","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1016\/j.ebiom.2018.07.036","volume":"34","author":"Z Zhang","year":"2018","unstructured":"Zhang Z, Yang T, Xiao J. Circular RNAs: promising biomarkers for human diseases. EBioMedicine. 2018;34:267\u201374. https:\/\/doi.org\/10.1016\/j.ebiom.2018.07.036.","journal-title":"EBioMedicine"},{"key":"4957_CR7","doi-asserted-by":"publisher","unstructured":"Pearson WR. An introduction to sequence similarity (\"homology\") searching. Curr Protoc Bioinformatics. 2013; Chapter 3: Unit3.1. https:\/\/doi.org\/10.1002\/0471250953.bi0301s42.","DOI":"10.1002\/0471250953.bi0301s42"},{"key":"4957_CR8","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1007\/978-981-13-1426-1_6","volume-title":"Circular RNAs: biogenesis and functions","author":"AC Panda","year":"2018","unstructured":"Panda AC. Circular RNAs act as miRNA sponges. In: Xiao J, editor. Circular RNAs: biogenesis and functions. Singapore: Springer; 2018. p. 67\u201379. https:\/\/doi.org\/10.1007\/978-981-13-1426-1_6."},{"issue":"1","key":"4957_CR9","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1093\/bib\/bbz175","volume":"22","author":"M Vromman","year":"2020","unstructured":"Vromman M, Vandesompele J, Volders P-J. Closing the circle: current state and perspectives of circular RNA databases. Brief Bioinform. 2020;22(1):288\u201397. https:\/\/doi.org\/10.1093\/bib\/bbz175.","journal-title":"Brief Bioinform"},{"issue":"1","key":"4957_CR10","doi-asserted-by":"publisher","first-page":"22165","DOI":"10.1038\/s41598-020-78469-x","volume":"10","author":"J Cardenas","year":"2020","unstructured":"Cardenas J, Balaji U, Gu J. Cerina: systematic circRNA functional annotation based on integrative analysis of ceRNA interactions. Sci Rep. 2020;10(1):22165. https:\/\/doi.org\/10.1038\/s41598-020-78469-x.","journal-title":"Sci Rep"},{"issue":"1","key":"4957_CR11","doi-asserted-by":"publisher","first-page":"10138","DOI":"10.1038\/s41598-020-66990-y","volume":"10","author":"S Li","year":"2020","unstructured":"Li S, Chen L, Xu C, Qu X, Qin Z, Gao J, Li J, Liu J. Expression profile and bioinformatics analysis of circular RNAs in acute ischemic stroke in a South Chinese Han population. Sci Rep. 2020;10(1):10138. https:\/\/doi.org\/10.1038\/s41598-020-66990-y.","journal-title":"Sci Rep"},{"key":"4957_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiolchem.2020.107287","volume":"87","author":"Y Ding","year":"2020","unstructured":"Ding Y, Chen B, Lei X, Liao B, Wu F-X. Predicting novel CircRNA-disease associations based on random walk and logistic regression model. Comput Biol Chem. 2020;87: 107287. https:\/\/doi.org\/10.1016\/j.compbiolchem.2020.107287.","journal-title":"Comput Biol Chem"},{"issue":"4","key":"4957_CR13","doi-asserted-by":"publisher","first-page":"261","DOI":"10.26599\/BDMA.2019.9020010","volume":"2","author":"Z Fang","year":"2019","unstructured":"Fang Z, Lei X. Prediction of miRNA-circRNA associations based on $$k$$-nn multi-label with random walk restart on a heterogeneous network. Big Data Min Anal. 2019;2(4):261\u201372.","journal-title":"Big Data Min Anal"},{"issue":"18","key":"4957_CR14","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1093\/bioinformatics\/bts389","volume":"28","author":"E Glaab","year":"2012","unstructured":"Glaab E, Baudot A, Krasnogor N, Schneider R, Valencia A. EnrichNet: network-based gene set enrichment analysis. Bioinformatics. 2012;28(18):451\u20137. https:\/\/doi.org\/10.1093\/bioinformatics\/bts389.","journal-title":"Bioinformatics"},{"issue":"1","key":"4957_CR15","doi-asserted-by":"publisher","first-page":"1943","DOI":"10.1038\/s41598-020-59040-0","volume":"10","author":"X Lei","year":"2020","unstructured":"Lei X, Bian C. Integrating random walk with restart and $$k$$-nearest neighbor to identify novel circRNA-disease association. Sci Rep. 2020;10(1):1943. https:\/\/doi.org\/10.1038\/s41598-020-59040-0.","journal-title":"Sci Rep"},{"issue":"6770","key":"4957_CR16","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1038\/35001165","volume":"403","author":"S Oliver","year":"2000","unstructured":"Oliver S. Guilt-by-association goes global. Nature. 2000;403(6770):601\u20132. https:\/\/doi.org\/10.1038\/35001165.","journal-title":"Nature"},{"issue":"1","key":"4957_CR17","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1111\/biom.12731","volume":"74","author":"BD Segal","year":"2018","unstructured":"Segal BD, Braun T, Elliott MR, Jiang H. Fast approximation of small $$p$$-values in permutation tests by partitioning the permutations. Biometrics. 2018;74(1):196\u2013206. https:\/\/doi.org\/10.1111\/biom.12731.","journal-title":"Biometrics"},{"issue":"2","key":"4957_CR18","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1080\/07474940902816601","volume":"28","author":"I Silva","year":"2009","unstructured":"Silva I, Assun\u00e7\u00e3o R, Costa M. Power of the sequential monte Carlo test. Seq Anal. 2009;28(2):163\u201374. https:\/\/doi.org\/10.1080\/07474940902816601.","journal-title":"Seq Anal"},{"key":"4957_CR19","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.jmva.2013.06.003","volume":"121","author":"IR Silva","year":"2013","unstructured":"Silva IR, Assun\u00e7\u00e3o RM. Optimal generalized truncated sequential monte Carlo test. J Multivar Anal. 2013;121:33\u201349. https:\/\/doi.org\/10.1016\/j.jmva.2013.06.003.","journal-title":"J Multivar Anal"},{"key":"4957_CR20","unstructured":"Feller W. Introduction to Probability Theory and Its Applications, (1966)"},{"key":"4957_CR21","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1007\/978-1-4939-7231-9_2","volume-title":"Functional genomics: methods and protocols","author":"Y Li","year":"2017","unstructured":"Li Y, Xu J, Shao T, Zhang Y, Chen H, Li X. RNA function prediction. In: Kaufmann M, Klinger C, Savelsbergh A, editors. Functional genomics: methods and protocols. New York, NY: Springer; 2017. p. 17\u201328. https:\/\/doi.org\/10.1007\/978-1-4939-7231-9_2."},{"key":"4957_CR22","doi-asserted-by":"crossref","unstructured":"Manly B, Navarro Alberto J. Randomization, Bootstrap and Monte Carlo methods in biology. 4th ed. London: Chapman and Hall\/CRC; 2020.","DOI":"10.1201\/9780429329203"},{"key":"4957_CR23","doi-asserted-by":"publisher","DOI":"10.2202\/1544-6115.1585","author":"B Phipson","year":"2010","unstructured":"Phipson B, Smyth GK. Permutation $$p$$-values should never be zero: calculating exact $$p$$-values when permutations are randomly drawn. Stat Appl Genet Mol Biol. 2010. https:\/\/doi.org\/10.2202\/1544-6115.1585.","journal-title":"Stat Appl Genet Mol Biol"},{"issue":"1","key":"4957_CR24","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1186\/1471-2105-8-290","volume":"8","author":"A Keller","year":"2007","unstructured":"Keller A, Backes C, Lenhof H-P. Computation of significance scores of unweighted gene set enrichment analyses. BMC Bioinform. 2007;8(1):290. https:\/\/doi.org\/10.1186\/1471-2105-8-290.","journal-title":"BMC Bioinform"},{"issue":"1","key":"4957_CR25","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1080\/00031305.2017.1375990","volume":"72","author":"D Eddelbuettel","year":"2018","unstructured":"Eddelbuettel D, Balamuta JJ. Extending R with C++: a brief introduction to Rcpp. Am Stat. 2018;72(1):28\u201336. https:\/\/doi.org\/10.1080\/00031305.2017.1375990.","journal-title":"Am Stat"},{"issue":"1","key":"4957_CR26","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1080\/15476286.2015.1128065","volume":"13","author":"DB Dudekula","year":"2016","unstructured":"Dudekula DB, Panda AC, Grammatikakis I, De S, Abdelmohsen K, Gorospe M. CircInteractome: a web tool for exploring circular RNAs and their interacting proteins and microRNAs. RNA Biol. 2016;13(1):34\u201342. https:\/\/doi.org\/10.1080\/15476286.2015.1128065.","journal-title":"RNA Biol"},{"issue":"1","key":"4957_CR27","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.cell.2004.12.035","volume":"120","author":"BP Lewis","year":"2005","unstructured":"Lewis BP, Burge CB, Bartel DP. Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microrna targets. Cell. 2005;120(1):15\u201320. https:\/\/doi.org\/10.1016\/j.cell.2004.12.035.","journal-title":"Cell"},{"issue":"D1","key":"4957_CR28","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1093\/nar\/gkx1141","volume":"46","author":"D Karagkouni","year":"2017","unstructured":"Karagkouni D, Paraskevopoulou MD, Chatzopoulos S, Vlachos IS, Tastsoglou S, Kanellos I, Papadimitriou D, Kavakiotis I, Maniou S, Skoufos G, Vergoulis T, Dalamagas T, Hatzigeorgiou AG. DIANA-TarBase v8: a decade-long collection of experimentally supported miRNA-gene interactions. Nucleic Acids Res. 2017;46(D1):239\u201345. https:\/\/doi.org\/10.1093\/nar\/gkx1141.","journal-title":"Nucleic Acids Res"},{"issue":"suppl\u20131","key":"4957_CR29","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1093\/nar\/gkn851","volume":"37","author":"F Xiao","year":"2008","unstructured":"Xiao F, Zuo Z, Cai G, Kang S, Gao X, Li T. miRecords: an integrated resource for microRNA-target interactions. Nucleic Acids Res. 2008;37(suppl\u20131):105\u201310. https:\/\/doi.org\/10.1093\/nar\/gkn851.","journal-title":"Nucleic Acids Res"},{"issue":"suppl\u20131","key":"4957_CR30","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1093\/nar\/gkq1107","volume":"39","author":"S-D Hsu","year":"2010","unstructured":"Hsu S-D, Lin F-M, Wu W-Y, Liang C, Huang W-C, Chan W-L, Tsai W-T, Chen G-Z, Lee C-J, Chiu C-M, Chien C-H, Wu M-C, Huang C-Y, Tsou A-P, Huang H-D. miRTarBase: a database curates experimentally validated microRNA-target interactions. Nucleic Acids Res. 2010;39(suppl\u20131):163\u20139. https:\/\/doi.org\/10.1093\/nar\/gkq1107.","journal-title":"Nucleic Acids Res"},{"issue":"17","key":"4957_CR31","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1093\/nar\/gku631","volume":"42","author":"Y Ru","year":"2014","unstructured":"Ru Y, Kechris KJ, Tabakoff B, Hoffman P, Radcliffe RA, Bowler R, Mahaffey S, Rossi S, Calin GA, Bemis L, Theodorescu D. The multiMiR R package and database: integration of microRNA-target interactions along with their disease and drug associations. Nucleic Acids Res. 2014;42(17):133. https:\/\/doi.org\/10.1093\/nar\/gku631.","journal-title":"Nucleic Acids Res"},{"issue":"suppl\u20131","key":"4957_CR32","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1093\/nar\/gkj112","volume":"34","author":"S Griffiths-Jones","year":"2006","unstructured":"Griffiths-Jones S, Grocock RJ, van Dongen S, Bateman A, Enright AJ. miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids Res. 2006;34(suppl\u20131):140\u20134. https:\/\/doi.org\/10.1093\/nar\/gkj112.","journal-title":"Nucleic Acids Res"},{"issue":"22","key":"4957_CR33","doi-asserted-by":"publisher","first-page":"3045","DOI":"10.1093\/bioinformatics\/btp536","volume":"25","author":"D Binns","year":"2009","unstructured":"Binns D, Dimmer E, Huntley R, Barrell D, O\u2019Donovan C, Apweiler R. QuickGO: a web-based tool for gene ontology searching. Bioinformatics. 2009;25(22):3045\u20136. https:\/\/doi.org\/10.1093\/bioinformatics\/btp536.","journal-title":"Bioinformatics"},{"issue":"12","key":"4957_CR34","doi-asserted-by":"publisher","first-page":"1739","DOI":"10.1093\/bioinformatics\/btr260","volume":"27","author":"A Liberzon","year":"2011","unstructured":"Liberzon A, Subramanian A, Pinchback R, Thorvaldsd\u00f3ttir H, Tamayo P, Mesirov JP. Molecular signatures database (MSigDB) 3.0. Bioinformatics. 2011;27(12):1739\u201340. https:\/\/doi.org\/10.1093\/bioinformatics\/btr260.","journal-title":"Bioinformatics"},{"issue":"2","key":"4957_CR35","doi-asserted-by":"publisher","first-page":"482","DOI":"10.1038\/s41596-018-0103-9","volume":"14","author":"J Reimand","year":"2019","unstructured":"Reimand J, Isserlin R, Voisin V, Kucera M, Tannus-Lopes C, Rostamianfar A, Wadi L, Meyer M, Wong J, Xu C, Merico D, Bader GD. Pathway enrichment analysis and visualization of omics data using g:Profiler, GSEA, Cytoscape and EnrichmentMap. Nat Protoc. 2019;14(2):482\u2013517. https:\/\/doi.org\/10.1038\/s41596-018-0103-9.","journal-title":"Nat Protoc"},{"issue":"293","key":"4957_CR36","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1080\/01621459.1961.10482090","volume":"56","author":"OJ Dunn","year":"1961","unstructured":"Dunn OJ. Multiple comparisons among means. J Am Stat Assoc. 1961;56(293):52\u201364. https:\/\/doi.org\/10.1080\/01621459.1961.10482090.","journal-title":"J Am Stat Assoc"},{"issue":"1","key":"4957_CR37","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","volume":"57","author":"Y Benjamini","year":"1995","unstructured":"Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B (Methodological). 1995;57(1):289\u2013300.","journal-title":"J R Stat Soc Ser B (Methodological)"},{"issue":"11","key":"4957_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0013984","volume":"5","author":"D Merico","year":"2010","unstructured":"Merico D, Isserlin R, Stueker O, Emili A, Bader GD. Enrichment map: a network-based method for gene-set enrichment visualization and interpretation. PLoS One. 2010;5(11):1\u201312. https:\/\/doi.org\/10.1371\/journal.pone.0013984.","journal-title":"PLoS One"},{"issue":"11","key":"4957_CR39","doi-asserted-by":"publisher","first-page":"2498","DOI":"10.1101\/gr.1239303","volume":"13","author":"P Shannon","year":"2003","unstructured":"Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498\u2013504. https:\/\/doi.org\/10.1101\/gr.1239303.","journal-title":"Genome Res"},{"issue":"11","key":"4957_CR40","doi-asserted-by":"publisher","first-page":"1666","DOI":"10.1261\/rna.043687.113","volume":"20","author":"P Gla\u017ear","year":"2014","unstructured":"Gla\u017ear P, Papavasileiou P, Rajewsky N. circBase: a database for circular RNAs. RNA. 2014;20(11):1666\u201370.","journal-title":"RNA"},{"issue":"2","key":"4957_CR41","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1074\/mcp.M113.035600","volume":"13","author":"L Fagerberg","year":"2014","unstructured":"Fagerberg L, Hallstr\u00f6m BM, Oksvold P, Kampf C, Djureinovic D, Odeberg J, Habuka M, Tahmasebpoor S, Danielsson A, Edlund K, Asplund A, Sj\u00f6stedt E, Lundberg E, Szigyarto CA-K, Skogs M, Takanen JO, Berling H, Tegel H, Mulder J, Nilsson P, Schwenk JM, Lindskog C, Danielsson F, Mardinoglu A, Sivertsson \u00c5, von Feilitzen K, Forsberg M, Zwahlen M, Olsson I, Navani S, Huss M, Nielsen J, Ponten F, Uhl\u00e9n M. Analysis of the human tissue-specific expression by genome-wide integration of transcriptomics and antibody-based proteomics. Mol Cell Proteom. 2014;13(2):397\u2013406. https:\/\/doi.org\/10.1074\/mcp.M113.035600.","journal-title":"Mol Cell Proteom"},{"issue":"1","key":"4957_CR42","doi-asserted-by":"publisher","first-page":"4397","DOI":"10.1080\/21655979.2021.1954846","volume":"12","author":"S Liu","year":"2021","unstructured":"Liu S, Li B, Li Y, Song H. Circular rna circ_0000228 promotes the malignancy of cervical cancer via microrna-195-5p\/ lysyl oxidase-like protein 2 axis. Bioengineered. 2021;12(1):4397\u2013406. https:\/\/doi.org\/10.1080\/21655979.2021.1954846.","journal-title":"Bioengineered"},{"key":"4957_CR43","doi-asserted-by":"publisher","first-page":"299","DOI":"10.3917\/droz.paret.1964.01","volume-title":"Cours Deconomie Politique","author":"V Pareto","year":"1964","unstructured":"Pareto V. Cours Deconomie Politique. Geneva: Librairie Droz; 1964. p. 299\u2013345."},{"key":"4957_CR44","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1007\/978-3-031-13829-4_39","volume-title":"Intelligent computing theories and application","author":"B-W Zhao","year":"2022","unstructured":"Zhao B-W, Hu L, Hu P-W, You Z-H, Su X-R, Li D-X, Chen Z-H, Zhang P. MRLDTI: a meta-path-based representation learning model for drug-target interaction prediction. In: Huang D-S, Jo K-H, Jing J, Premaratne P, Bevilacqua V, Hussain A, editors. Intelligent computing theories and application. Cham: Springer; 2022. p. 451\u20139. https:\/\/doi.org\/10.1007\/978-3-031-13829-4_39."},{"key":"4957_CR45","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1007\/978-3-031-13829-4_18","volume-title":"Intelligent computing theories and application","author":"M-L Zhang","year":"2022","unstructured":"Zhang M-L, Zhao B-W, Hu L, You Z-H, Chen Z-H. Predicting drug-disease associations via meta-path representation learning based on heterogeneous information net works. In: Huang D-S, Jo K-H, Jing J, Premaratne P, Bevilacqua V, Hussain A, editors. Intelligent computing theories and application. Cham: Springer; 2022. p. 220\u201332. https:\/\/doi.org\/10.1007\/978-3-031-13829-4_18."},{"key":"4957_CR46","doi-asserted-by":"publisher","unstructured":"Vural H, Kaya M, Alhajj R. A model based on random walk with restart to predict circRNA-disease associations on heterogeneous network. In: Proceedings of the 2019 IEEE\/ACM international conference on advances in social networks analysis and mining. ASONAM \u201919, pp. 929\u2013932. Association for Computing Machinery, New York, NY, USA (2019). https:\/\/doi.org\/10.1145\/3341161.3343514. https:\/\/doi.org\/10.1145\/3341161.3343514","DOI":"10.1145\/3341161.3343514"},{"key":"4957_CR47","doi-asserted-by":"publisher","unstructured":"Brin S, Page L. The anatomy of a large-scale hypertextual web search engine. Computer Networks and ISDN Systems. 1998;30(1):107\u201317. https:\/\/doi.org\/10.1016\/S0169-7552(98)00110-X (Proceedings of the Seventh International World Wide Web Conference).","DOI":"10.1016\/S0169-7552(98)00110-X"},{"issue":"suppl\u20131","key":"4957_CR48","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1093\/nar\/gkn714","volume":"37","author":"Q Jiang","year":"2008","unstructured":"Jiang Q, Wang Y, Hao Y, Juan L, Teng M, Zhang X, Li M, Wang G, Liu Y. miR2Disease: a manually curated database for microRNA deregulation in human disease. Nucleic Acids Res. 2008;37(suppl\u20131):98\u2013104. https:\/\/doi.org\/10.1093\/nar\/gkn714.","journal-title":"Nucleic Acids Res"},{"issue":"D1","key":"4957_CR49","doi-asserted-by":"publisher","first-page":"1013","DOI":"10.1093\/nar\/gky1010","volume":"47","author":"Z Huang","year":"2018","unstructured":"Huang Z, Shi J, Gao Y, Cui C, Zhang S, Li J, Zhou Y, Cui Q. HMDD v3.0: a database for experimentally supported human microRNA-disease associations. Nucleic Acids Res. 2018;47(D1):1013\u20137. https:\/\/doi.org\/10.1093\/nar\/gky1010.","journal-title":"Nucleic Acids Res"},{"key":"4957_CR50","doi-asserted-by":"publisher","unstructured":"Pi\u00f1ero J, Queralt-Rosinach N, Bravo A, Deu-Pons J, Bauer-Mehren A, Baron M, Sanz F, Furlong LI. DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes. Database, 2015; 2015. https:\/\/doi.org\/10.1093\/database\/bav028. bav028. https:\/\/academic.oup.com\/database\/article-pdf\/doi\/10.1093\/database\/bav028\/16975988\/bav028.pdf","DOI":"10.1093\/database\/bav028"},{"key":"4957_CR51","doi-asserted-by":"publisher","unstructured":"Lan W, Zhu M, Chen Q, Chen B, Liu J, Li M, Chen Y-PP. CircR2Cancer: a manually curated database of associations between circRNAs and cancers. Database, 2020; 2020. https:\/\/doi.org\/10.1093\/database\/baaa085. baaa085. https:\/\/academic.oup.com\/database\/article-pdf\/doi\/10.1093\/database\/baaa085\/34283838\/baaa085.pdf","DOI":"10.1093\/database\/baaa085"},{"issue":"1","key":"4957_CR52","doi-asserted-by":"publisher","first-page":"11018","DOI":"10.1038\/s41598-018-29360-3","volume":"8","author":"D Yao","year":"2018","unstructured":"Yao D, Zhang L, Zheng M, Sun X, Lu Y, Liu P. Circ2Disease: a manually curated database of experimentally validated circRNAs in human disease. Sci Rep. 2018;8(1):11018. https:\/\/doi.org\/10.1038\/s41598-018-29360-3.","journal-title":"Sci Rep"},{"key":"4957_CR53","doi-asserted-by":"publisher","unstructured":"Fan C, Lei X, Fang Z, Jiang Q, Wu F-X. CircR2Disease: a manually curated database for experimentally supported circular RNAs associated with various diseases. Database. 2018. https:\/\/doi.org\/10.1093\/database\/bay044.","DOI":"10.1093\/database\/bay044"},{"key":"4957_CR54","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2013.00283","author":"S Ghosal","year":"2013","unstructured":"Ghosal S, Das S, Sen R, Basak P, Chakrabarti J. Circ2Traits: a comprehensive database for circular RNA potentially associated with disease and traits. Front Genet. 2013. https:\/\/doi.org\/10.3389\/fgene.2013.00283.","journal-title":"Front Genet"},{"issue":"11","key":"4957_CR55","doi-asserted-by":"publisher","first-page":"3410","DOI":"10.3390\/ijms19113410","volume":"19","author":"X Lei","year":"2018","unstructured":"Lei X, Fang Z, Chen L, Wu F-X. PWCDA: path weighted method for predicting circRNA-disease associations. Int J Mol Sci. 2018;19(11):3410. https:\/\/doi.org\/10.3390\/ijms19113410.","journal-title":"Int J Mol Sci"},{"issue":"4","key":"4957_CR56","doi-asserted-by":"publisher","first-page":"578","DOI":"10.1109\/TNB.2019.2922214","volume":"18","author":"Q Zhao","year":"2019","unstructured":"Zhao Q, Yang Y, Ren G, Ge E, Fan C. Integrating bipartite network projection and KATZ measure to identify novel circRNA-disease associations. IEEE Trans Nanobiosci. 2019;18(4):578\u201384. https:\/\/doi.org\/10.1109\/TNB.2019.2922214.","journal-title":"IEEE Trans Nanobiosci"},{"key":"4957_CR57","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2019.00897","author":"X-J Lei","year":"2019","unstructured":"Lei X-J, Fang Z, Guo L. Predicting circRNA-disease associations based on improved collaboration filtering recommendation system with multiple data. Front Genet. 2019. https:\/\/doi.org\/10.3389\/fgene.2019.00897.","journal-title":"Front Genet"},{"issue":"24","key":"4957_CR58","doi-asserted-by":"publisher","first-page":"5656","DOI":"10.1093\/bioinformatics\/btaa1077","volume":"36","author":"C Lu","year":"2020","unstructured":"Lu C, Zeng M, Wu F-X, Li M, Wang J. Improving circRNA-disease association prediction by sequence and ontology representations with convolutional and recurrent neural networks. Bioinformatics. 2020;36(24):5656\u201364. https:\/\/doi.org\/10.1093\/bioinformatics\/btaa1077.","journal-title":"Bioinformatics"},{"key":"4957_CR59","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbac083","author":"H-Y Zhang","year":"2022","unstructured":"Zhang H-Y, Wang L, You Z-H, Hu L, Zhao B-W, Li Z-W, Li Y-M. iGRLCDA: identifying circRNA-disease association based on graph representation learning. Brief Bioinform. 2022. https:\/\/doi.org\/10.1093\/bib\/bbac083.","journal-title":"Brief Bioinform"},{"key":"4957_CR60","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbab515","author":"B-W Zhao","year":"2021","unstructured":"Zhao B-W, Hu L, You Z-H, Wang L, Su X-R. HINGRL: predicting drug-disease associations with graph representation learning on heterogeneous information networks. Brief Bioinform. 2021. https:\/\/doi.org\/10.1093\/bib\/bbab515.","journal-title":"Brief Bioinform"},{"key":"4957_CR61","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2020.103624","volume":"112","author":"G Li","year":"2020","unstructured":"Li G, Luo J, Wang D, Liang C, Xiao Q, Ding P, Chen H. Potential circRNA-disease association prediction using DeepWalk and network consistency projection. J Biomed Inform. 2020;112: 103624. https:\/\/doi.org\/10.1016\/j.jbi.2020.103624.","journal-title":"J Biomed Inform"},{"issue":"57","key":"4957_CR62","doi-asserted-by":"publisher","first-page":"33222","DOI":"10.1039\/C9RA06133A","volume":"9","author":"G Li","year":"2019","unstructured":"Li G, Yue Y, Liang C, Xiao Q, Ding P, Luo J. NCPCDA: network consistency projection for circRNA-disease association prediction. RSC Adv. 2019;9(57):33222\u20138. https:\/\/doi.org\/10.1039\/C9RA06133A.","journal-title":"RSC Adv"},{"issue":"4","key":"4957_CR63","doi-asserted-by":"publisher","first-page":"280","DOI":"10.26599\/BDMA.2020.9020025","volume":"3","author":"Y Zhang","year":"2020","unstructured":"Zhang Y, Lei X, Fang Z, Pan Y. Circrna-disease associations prediction based on metapath2vec++ and matrix factorization. Big Data Min Anal. 2020;3(4):280\u201391. https:\/\/doi.org\/10.26599\/BDMA.2020.9020025.","journal-title":"Big Data Min Anal"},{"key":"4957_CR64","doi-asserted-by":"publisher","first-page":"145040","DOI":"10.1016\/j.gene.2020.145040","volume":"762","author":"K Deepthi","year":"2020","unstructured":"Deepthi K, Jereesh AS. An ensemble approach for circrna-disease association prediction based on autoencoder and deep neural network. Gene. 2020;762:145040. https:\/\/doi.org\/10.1016\/j.gene.2020.145040.","journal-title":"Gene"},{"key":"4957_CR65","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2021.3090756","author":"L Wang","year":"2021","unstructured":"Wang L, You Z-H, Huang D-S, Li J-Q. MGRCDA: Metagraph recommendation method for predicting circRNA-disease association. IEEE Trans Cybern. 2021. https:\/\/doi.org\/10.1109\/TCYB.2021.3090756.","journal-title":"IEEE Trans Cybern"},{"issue":"5","key":"4957_CR66","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pcbi.1007872","volume":"16","author":"K Zheng","year":"2020","unstructured":"Zheng K, You Z-H, Li J-Q, Wang L, Guo Z-H, Huang Y-A. iCDA-CGR: Identification of circRNA-disease associations based on chaos game representation. PLoS Comput Biol. 2020;16(5):1\u201322. https:\/\/doi.org\/10.1371\/journal.pcbi.1007872.","journal-title":"PLoS Comput Biol"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-022-04957-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-022-04957-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-022-04957-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,4]],"date-time":"2024-10-04T19:09:10Z","timestamp":1728068950000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-022-04957-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,27]]},"references-count":66,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["4957"],"URL":"https:\/\/doi.org\/10.1186\/s12859-022-04957-8","relation":{},"ISSN":["1471-2105"],"issn-type":[{"type":"electronic","value":"1471-2105"}],"subject":[],"published":{"date-parts":[[2022,9,27]]},"assertion":[{"value":"20 May 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 September 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 September 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"392"}}