{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T16:01:43Z","timestamp":1742918503559,"version":"3.40.3"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031138317"},{"type":"electronic","value":"9783031138324"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-13832-4_52","type":"book-chapter","created":{"date-parts":[[2022,8,15]],"date-time":"2022-08-15T11:32:22Z","timestamp":1660563142000},"page":"639-653","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Unified Graph Attention Network Based Framework for Inferring circRNA-Disease Associations"],"prefix":"10.1007","author":[{"given":"Cun-Mei","family":"Ji","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhi-Hao","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li-Juan","family":"Qiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu-Tian","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chun-Hou","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,8,16]]},"reference":[{"issue":"8","key":"52_CR1","doi-asserted-by":"publisher","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","volume":"35","author":"Y Bengio","year":"2013","unstructured":"Bengio, Y., et al.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798\u20131828 (2013)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"52_CR2","doi-asserted-by":"crossref","unstructured":"Bian, C., et al.: GATCDA: predicting circRNA-disease associations based on graph attention network. Cancers (Basel) 13(11), 2595 (2021)","DOI":"10.3390\/cancers13112595"},{"issue":"2018","key":"52_CR3","first-page":"1","volume":"2018","author":"C Fan","year":"2018","unstructured":"Fan, C., et al.: CircR2Disease: a manually curated database for experimentally supported circular RNAs associated with various diseases. Database 2018(2018), 1\u20136 (2018)","journal-title":"Database"},{"issue":"14","key":"52_CR4","doi-asserted-by":"publisher","first-page":"1950","DOI":"10.7150\/ijbs.28260","volume":"14","author":"C Fan","year":"2018","unstructured":"Fan, C., et al.: Prediction of circRNA-disease associations using KATZ model based on heterogeneous networks. Int. J. Biol. Sci. 14(14), 1950\u20131959 (2018)","journal-title":"Int. J. Biol. Sci."},{"key":"52_CR5","unstructured":"Fey, M., Lenssen, J.E.: Fast graph representation learning with PyTorch geometric. arXiv. 1, 1\u20139 (2019)"},{"issue":"7","key":"52_CR6","doi-asserted-by":"publisher","first-page":"5156","DOI":"10.1007\/s12035-016-0055-4","volume":"54","author":"G Floris","year":"2017","unstructured":"Floris, G., et al.: Regulatory role of circular RNAs and neurological disorders. Mol. Neurobiol. 54(7), 5156\u20135165 (2017)","journal-title":"Mol. Neurobiol."},{"issue":"2","key":"52_CR7","doi-asserted-by":"publisher","first-page":"1335","DOI":"10.1016\/j.ygeno.2019.08.001","volume":"112","author":"E Ge","year":"2020","unstructured":"Ge, E., et al.: Predicting human disease-associated circRNAs based on locality-constrained linear coding. Genomics 112(2), 1335\u20131342 (2020)","journal-title":"Genomics"},{"issue":"6","key":"52_CR8","doi-asserted-by":"publisher","first-page":"1071","DOI":"10.1007\/s00018-017-2688-5","volume":"75","author":"LM Holdt","year":"2017","unstructured":"Holdt, L.M., Kohlmaier, A., Teupser, D.: Molecular roles and function of circular RNAs in eukaryotic cells. Cell. Mol. Life Sci. 75(6), 1071\u20131098 (2017). https:\/\/doi.org\/10.1007\/s00018-017-2688-5","journal-title":"Cell. Mol. Life Sci."},{"key":"52_CR9","unstructured":"Kingma, D.P., Ba, J.L.: Adam: a method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings (2015)"},{"issue":"11","key":"52_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/ijms19113410","volume":"19","author":"X Lei","year":"2018","unstructured":"Lei, X., et al.: Pwcda: path weighted method for predicting circrna-disease associations. Int. J. Mol. Sci. 19(11), 1\u201313 (2018)","journal-title":"Int. J. Mol. Sci."},{"issue":"57","key":"52_CR11","doi-asserted-by":"publisher","first-page":"33222","DOI":"10.1039\/C9RA06133A","volume":"9","author":"G Li","year":"2019","unstructured":"Li, G., et al.: NCPCDA: network consistency projection for circRNA-disease association prediction. RSC Adv. 9(57), 33222\u201333228 (2019)","journal-title":"RSC Adv."},{"issue":"3","key":"52_CR12","doi-asserted-by":"publisher","first-page":"891","DOI":"10.1109\/JBHI.2020.2999638","volume":"25","author":"C Lu","year":"2020","unstructured":"Lu, C., et al.: Deep matrix factorization improves prediction of human circRNA-disease associations. IEEE J. Biomed. Heal. Inform. 25(3), 891\u2013899 (2020)","journal-title":"IEEE J. Biomed. Heal. Inform."},{"issue":"7441","key":"52_CR13","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1038\/nature11928","volume":"495","author":"S Memczak","year":"2013","unstructured":"Memczak, S., et al.: Circular RNAs are a large class of animal RNAs with regulatory potency. Nature 495(7441), 333\u2013338 (2013)","journal-title":"Nature"},{"key":"52_CR14","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1016\/j.gde.2017.11.007","volume":"48","author":"IL Patop","year":"2018","unstructured":"Patop, I.L., Kadener, S.: circRNAs in cancer. Curr. Opin. Genet. Dev. 48, 121\u2013127 (2018)","journal-title":"Curr. Opin. Genet. Dev."},{"key":"52_CR15","unstructured":"Veli\u010dkovi\u0107, P., et al.: Graph attention networks. In: 6th International Conference on Learning Representations. ICLR 2018 \u2013 Conference on Track Proceedings, pp. 1\u201312 (2018)"},{"issue":"6","key":"52_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1093\/bib\/bbab286","volume":"22","author":"CC Wang","year":"2021","unstructured":"Wang, C.C., et al.: Circular RNAs and complex diseases: from experimental results to computational models. Brief. Bioinform. 22(6), 1\u201327 (2021)","journal-title":"Brief. Bioinform."},{"issue":"13","key":"52_CR17","doi-asserted-by":"publisher","first-page":"1644","DOI":"10.1093\/bioinformatics\/btq241","volume":"26","author":"D Wang","year":"2010","unstructured":"Wang, D., et al.: Inferring the human microRNA functional similarity and functional network based on microRNA-associated diseases. Bioinformatics 26(13), 1644\u20131650 (2010)","journal-title":"Bioinformatics"},{"issue":"13","key":"52_CR18","doi-asserted-by":"publisher","first-page":"4038","DOI":"10.1093\/bioinformatics\/btz825","volume":"36","author":"L Wang","year":"2020","unstructured":"Wang, L., et al.: An efficient approach based on multi-sources information to predict circRNA-disease associations using deep convolutional neural network. Bioinformatics 36(13), 4038\u20134046 (2020)","journal-title":"Bioinformatics"},{"key":"52_CR19","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1371\/journal.pcbi.1007568","volume":"16","author":"L Wang","year":"2020","unstructured":"Wang, L., et al.: GCNCDA: a new method for predicting circRNA-disease associations based on graph convolutional network algorithm. PLoS Comput. Biol. 16, 5 (2020)","journal-title":"PLoS Comput. Biol."},{"issue":"5","key":"52_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pcbi.1007568","volume":"16","author":"L Wang","year":"2020","unstructured":"Wang, L., et al.: GCNCDA: a new method for predicting circRNA-disease associations based on graph convolutional network algorithm. PLoS Comput. Biol. 16(5), 1\u201319 (2020)","journal-title":"PLoS Comput. Biol."},{"key":"52_CR21","doi-asserted-by":"crossref","unstructured":"Wang, L., et al.: Predicting circRNA-disease associations using deep generative adversarial network based on multi-source fusion information. In: 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 145\u2013152 IEEE (2019)","DOI":"10.1109\/BIBM47256.2019.8983411"},{"issue":"4","key":"52_CR22","doi-asserted-by":"publisher","first-page":"1356","DOI":"10.1093\/bib\/bbz057","volume":"21","author":"H Wei","year":"2019","unstructured":"Wei, H., Liu, B.: iCircDA-MF: identification of circRNA-disease associations based on matrix factorization. Brief. Bioinform. 21(4), 1356\u20131367 (2019)","journal-title":"Brief. Bioinform."},{"issue":"1","key":"52_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13059-020-02018-y","volume":"21","author":"W Wu","year":"2020","unstructured":"Wu, W., et al.: CircAtlas: an integrated resource of one million highly accurate circular RNAs from 1070 vertebrate transcriptomes. Genome Biol. 21(1), 1\u201314 (2020)","journal-title":"Genome Biol."},{"key":"52_CR24","doi-asserted-by":"crossref","unstructured":"Xia, Y., et al.: GraphBind: protein structural context embedded rules learned by hierarchical graph neural networks for recognizing nucleic-acid-binding residues, 49, 9 (2021)","DOI":"10.1093\/nar\/gkab044"},{"issue":"6","key":"52_CR25","doi-asserted-by":"publisher","first-page":"2661","DOI":"10.1109\/JBHI.2019.2891779","volume":"23","author":"Q Xiao","year":"2019","unstructured":"Xiao, Q., et al.: Computational prediction of human disease-associated circRNAs based on manifold regularization learning framework. IEEE J. Biomed. Heal. informatics. 23(6), 2661\u20132669 (2019)","journal-title":"IEEE J. Biomed. Heal. informatics."},{"issue":"1","key":"52_CR26","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1007\/s00438-020-01741-2","volume":"296","author":"Q Xiao","year":"2020","unstructured":"Xiao, Q., Zhong, J., Tang, X., Luo, J.: iCDA-CMG: identifying circRNA-disease associations by federating multi-similarity fusion and collective matrix completion. Mol. Genet. Genom. 296(1), 223\u2013233 (2020). https:\/\/doi.org\/10.1007\/s00438-020-01741-2","journal-title":"Mol. Genet. Genom."},{"issue":"19","key":"52_CR27","first-page":"73","volume":"19","author":"C Yan","year":"2018","unstructured":"Yan, C., et al.: DWNN-RLS: regularized least squares method for predicting circRNA-disease associations. BMC Bioinform. 19(19), 73\u201381 (2018)","journal-title":"BMC Bioinform."},{"issue":"3","key":"52_CR28","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1007\/s12079-020-00570-7","volume":"14","author":"Q Yan","year":"2020","unstructured":"Yan, Q., He, X., Kuang, G., Ou, C.: CircRNA cPWWP2A: an emerging player in diabetes mellitus. J. Cell Commun. Signal. 14(3), 351\u2013353 (2020). https:\/\/doi.org\/10.1007\/s12079-020-00570-7","journal-title":"J. Cell Commun. Signal."},{"issue":"10","key":"52_CR29","first-page":"4164","volume":"23","author":"M Yang","year":"2019","unstructured":"Yang, M., et al.: Circ-CCDC66 accelerates proliferation and invasion of gastric cancer via binding to miRNA-1238-3p. Eur. Rev. Med. Pharmacol. Sci. 23(10), 4164\u20134172 (2019)","journal-title":"Eur. Rev. Med. Pharmacol. Sci."},{"issue":"5","key":"52_CR30","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1038\/s41419-018-0503-3","volume":"9","author":"Z Zhao","year":"2018","unstructured":"Zhao, Z., et al.: CircRNA disease: a manually curated database of experimentally supported circRNA-disease associations. Cell Death Dis. 9(5), 4\u20135 (2018)","journal-title":"Cell Death Dis."},{"key":"52_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/ncomms11215","volume":"7","author":"Q Zheng","year":"2016","unstructured":"Zheng, Q., et al.: Circular RNA profiling reveals an abundant circHIPK3 that regulates cell growth by sponging multiple miRNAs. Nat. Commun. 7, 1\u201313 (2016)","journal-title":"Nat. Commun."},{"key":"52_CR32","doi-asserted-by":"crossref","unstructured":"Zhou, J., et al.: Graph neural networks: a review of methods and applications. AI Open 1(September 2020), 57\u201381 (2020)","DOI":"10.1016\/j.aiopen.2021.01.001"}],"container-title":["Lecture Notes in Computer Science","Intelligent Computing Methodologies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-13832-4_52","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T14:12:02Z","timestamp":1710339122000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-13832-4_52"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031138317","9783031138324"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-13832-4_52","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"16 August 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xi'an","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 August 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 August 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/2022\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Open","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"IC-ICC-CN","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"449","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"209","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"47% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}