{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T06:05:25Z","timestamp":1743141925849,"version":"3.40.3"},"publisher-location":"Cham","reference-count":38,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031138287"},{"type":"electronic","value":"9783031138294"}],"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\/978-3-031-13829-4_14","type":"book-chapter","created":{"date-parts":[[2022,8,14]],"date-time":"2022-08-14T09:03:13Z","timestamp":1660467793000},"page":"166-180","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["GCNMFCDA: A Method Based on Graph Convolutional Network and Matrix Factorization for Predicting circRNA-Disease Associations"],"prefix":"10.1007","author":[{"given":"Dian-Xiao","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cun-Mei","family":"Ji","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu-Tian","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian-Cheng","family":"Ni","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,8,15]]},"reference":[{"issue":"7441","key":"14_CR1","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"},{"issue":"5","key":"14_CR2","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1038\/nbt.2890","volume":"32","author":"WR Jeck","year":"2014","unstructured":"Jeck, W.R., Sharpless, N.E.: Detecting and characterizing circular RNAs. Nat. Biotechnol. 32(5), 453\u2013461 (2014)","journal-title":"Nat. Biotechnol."},{"issue":"7441","key":"14_CR3","doi-asserted-by":"publisher","first-page":"384","DOI":"10.1038\/nature11993","volume":"495","author":"TB Hansen","year":"2013","unstructured":"Hansen, T.B., et al.: Natural RNA circles function as efficient microRNA sponges. Nature 495(7441), 384\u2013388 (2013)","journal-title":"Nature"},{"issue":"9","key":"14_CR4","doi-asserted-by":"publisher","first-page":"614","DOI":"10.1007\/BF03401761","volume":"4","author":"CW Chao","year":"1998","unstructured":"Chao, C.W., Chan, D.C., Kuo, A., Leder, P.: The mouse formin (Fmn) gene: abundant circular RNA transcripts and gene-targeted deletion analysis. Mol. Med. 4(9), 614\u2013628 (1998)","journal-title":"Mol. Med."},{"issue":"3","key":"14_CR5","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1080\/15476286.2017.1279788","volume":"14","author":"K Abdelmohsen","year":"2017","unstructured":"Abdelmohsen, K., et al.: Identification of HuR target circular RNAs uncovers suppression of PABPN1 translation by CircPABPN1. RNA Biol. 14(3), 361\u2013369 (2017)","journal-title":"RNA Biol."},{"issue":"1","key":"14_CR6","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1016\/j.molcel.2014.08.019","volume":"56","author":"R Ashwal Fluss","year":"2014","unstructured":"Ashwal Fluss, R., et al.: circRNA biogenesis competes with pre-mRNA splicing. Mol. Cell 56(1), 55\u201366 (2014)","journal-title":"Mol. Cell"},{"issue":"1","key":"14_CR7","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1080\/15476286.2015.1128065","volume":"13","author":"DB Dudekula","year":"2016","unstructured":"Dudekula, D.B., Panda, A.C., Grammatikakis, I., De, S., Abdelmohsen, K., Gorospe, M.: CircInteractome: a web tool for exploring circular RNAs and their interacting proteins and microRNAs. RNA Biol. 13(1), 34\u201342 (2016)","journal-title":"RNA Biol."},{"issue":"1","key":"14_CR8","doi-asserted-by":"publisher","first-page":"11","DOI":"10.20892\/j.issn.2095-3941.2018.0425","volume":"16","author":"J Liu","year":"2019","unstructured":"Liu, J., Zhao, K., Huang, N., Zhang, N.: Circular RNAs and human glioma. Cancer Biol. Med. 16(1), 11 (2019)","journal-title":"Cancer Biol. Med."},{"key":"14_CR9","doi-asserted-by":"publisher","first-page":"105322","DOI":"10.1016\/j.compbiomed.2022.105322","volume":"143","author":"Y Chen","year":"2022","unstructured":"Chen, Y., Wang, Y., Ding, Y., Su, X., Wang, C.: RGCNCDA: Relational graph convolutional network improves circRNA-disease association prediction by incorporating microRNAs. Comput. Biol. Med. 143, 105322 (2022)","journal-title":"Comput. Biol. Med."},{"issue":"1","key":"14_CR10","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1007\/s40291-020-00499-y","volume":"25","author":"K Deepthi","year":"2021","unstructured":"Deepthi, K., Jereesh, A.S.: Inferring potential CircRNA\u2013disease associations via deep autoencoder-based classification. Mol. Diagn. Ther. 25(1), 87\u201397 (2021)","journal-title":"Mol. Diagn. Ther."},{"issue":"5","key":"14_CR11","doi-asserted-by":"publisher","first-page":"bbab028","DOI":"10.1093\/bib\/bbab028","volume":"22","author":"L Wang","year":"2021","unstructured":"Wang, L., Yan, X., You, Z.H., Zhou, X., Li, H.-Y., Huang, Y.-A.: SGANRDA: semi-supervised generative adversarial networks for predicting circRNA\u2013disease associations. Brief Bioinform. 22(5), bbab028 (2021)","journal-title":"Brief Bioinform."},{"key":"14_CR12","doi-asserted-by":"publisher","first-page":"bay044","DOI":"10.1093\/database\/bay044","volume":"2018","author":"C Fan","year":"2018","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, bay044 (2018)","journal-title":"Database"},{"issue":"4","key":"14_CR13","doi-asserted-by":"publisher","first-page":"608","DOI":"10.1093\/bioinformatics\/btu684","volume":"31","author":"G Yu","year":"2015","unstructured":"Yu, G., Wang, L.G., Yan, G.R., He, Q.Y.: DOSE: an R\/Bioconductor package for disease ontology semantic and enrichment analysis. Bioinformatics 31(4), 608\u2013609 (2015)","journal-title":"Bioinformatics"},{"issue":"13","key":"14_CR14","doi-asserted-by":"publisher","first-page":"1644","DOI":"10.1093\/bioinformatics\/btq241","volume":"26","author":"D Wang","year":"2010","unstructured":"Wang, D., Wang, J., Lu, M., Song, F., Cui, Q.: Inferring the human microRNA functional similarity and functional network based on microRNA-associated diseases. Bioinformatics 26(13), 1644\u20131650 (2010)","journal-title":"Bioinformatics"},{"issue":"21","key":"14_CR15","doi-asserted-by":"publisher","first-page":"3036","DOI":"10.1093\/bioinformatics\/btr500","volume":"27","author":"T van Laarhoven","year":"2011","unstructured":"van Laarhoven, T., Nabuurs, S.B., Marchiori, E.: Gaussian interaction profile kernels for predicting drug\u2013target interaction. Bioinformatics 27(21), 3036\u20133043 (2011)","journal-title":"Bioinformatics"},{"key":"14_CR16","unstructured":"Kipf, T.N., Welling. M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)"},{"issue":"8","key":"14_CR17","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1109\/MC.2009.263","volume":"42","author":"Y Koren","year":"2009","unstructured":"Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30\u201337 (2009)","journal-title":"Computer"},{"issue":"4","key":"14_CR18","doi-asserted-by":"publisher","first-page":"308","DOI":"10.26599\/BDMA.2018.9020008","volume":"1","author":"A Ramlatchan","year":"2018","unstructured":"Ramlatchan, A., Yang, M., Liu, Q., Li, M., Wang, J., Li, Y.: A survey of matrix completion methods for recommendation systems. Big Data Min. Analytics 1(4), 308\u2013323 (2018)","journal-title":"Big Data Min. Analytics"},{"key":"14_CR19","doi-asserted-by":"publisher","first-page":"897","DOI":"10.3389\/fgene.2019.00897","volume":"10","author":"X Lei","year":"2019","unstructured":"Lei, X., Fang, Z., Guo, L.: Predicting circRNA-disease associations based on improved collaboration filtering recommendation system with multiple data. Front Genet. 10, 897 (2019)","journal-title":"Front Genet."},{"issue":"23","key":"14_CR20","doi-asserted-by":"publisher","first-page":"9070","DOI":"10.3390\/ijms21239070","volume":"21","author":"NQK Le","year":"2020","unstructured":"Le, N.Q.K., Do, D.T., Hung, T.N.K., Lam, L.H.T., Huynh, T.T., Nguyen, N.T.K.: A computational framework based on ensemble deep neural networks for essential genes identification. Int. J. Mol. Sci. 21(23), 9070 (2020)","journal-title":"Int. J. Mol. Sci."},{"issue":"10","key":"14_CR21","doi-asserted-by":"publisher","first-page":"325","DOI":"10.3390\/biology9100325","volume":"9","author":"L Ho Thanh Lam","year":"2020","unstructured":"Ho Thanh Lam, L., et al.: Machine learning model for identifying antioxidant proteins using features calculated from primary sequences. Biology 9(10), 325 (2020)","journal-title":"Biology"},{"issue":"19","key":"14_CR22","first-page":"73","volume":"19","author":"C Yan","year":"2018","unstructured":"Yan, C., Wang, J., Wu, F.X.: DWNN-RLS: regularized least squares method for predicting circRNA-disease associations. BMC Bioinform. 19(19), 73\u201381 (2018)","journal-title":"BMC Bioinform."},{"issue":"11","key":"14_CR23","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. 19(11), 3410 (2018)","journal-title":"Int. J. Mol. Sci."},{"issue":"14","key":"14_CR24","doi-asserted-by":"publisher","first-page":"1950","DOI":"10.7150\/ijbs.28260","volume":"14","author":"C Fan","year":"2018","unstructured":"Fan, C., Lei, X., Wu, F.X.: Prediction of CircRNA-disease associations using KATZ model based on heterogeneous networks. Int. J. Biol. Sci. 14(14), 1950 (2018)","journal-title":"Int. J. Biol. Sci."},{"issue":"5","key":"14_CR25","doi-asserted-by":"publisher","first-page":"e1007568","DOI":"10.1371\/journal.pcbi.1007568","volume":"16","author":"L Wang","year":"2020","unstructured":"Wang, L., You, Z.H., Li, Y.M., Zheng, K., Huang, Y.A.: GCNCDA: a new method for predicting circRNA-disease associations based on graph convolutional network algorithm. PLoS Comput. Biol. 16(5), e1007568 (2020)","journal-title":"PLoS Comput. Biol."},{"key":"14_CR26","doi-asserted-by":"publisher","first-page":"e1007872","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. 16, e1007872 (2020)","journal-title":"PLOS Comput. Biol."},{"issue":"4","key":"14_CR27","doi-asserted-by":"publisher","first-page":"1356","DOI":"10.1093\/bib\/bbz057","volume":"21","author":"H Wei","year":"2020","unstructured":"Wei, H., Liu, B.: iCircDA-MF: identification of circRNA-disease associations based on matrix factorization. Brief Bioinform. 21(4), 1356\u20131367 (2020)","journal-title":"Brief Bioinform."},{"key":"14_CR28","doi-asserted-by":"publisher","first-page":"83474","DOI":"10.1109\/ACCESS.2019.2920942","volume":"7","author":"W Zhang","year":"2019","unstructured":"Zhang, W., Chenglin, Y., Wang, X., Liu, F.: Predicting CircRNA-disease associations through linear neighborhood label propagation method. IEEE Access 7, 83474\u201383483 (2019)","journal-title":"IEEE Access"},{"issue":"3","key":"14_CR29","doi-asserted-by":"publisher","first-page":"891","DOI":"10.1109\/JBHI.2020.2999638","volume":"25","author":"C Lu","year":"2021","unstructured":"Lu, C., Zeng, M., Zhang, F., Wu, F.-X., Li, M., Wang, J.: Deep matrix factorization improves prediction of human circRNA-disease associations. IEEE J. Biomed. Health Inform. 25(3), 891\u2013899 (2021)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"14_CR30","doi-asserted-by":"crossref","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, pp. 929\u2013932 (2019)","DOI":"10.1145\/3341161.3343514"},{"issue":"57","key":"14_CR31","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. 9(57), 33222\u201333228 (2019)","journal-title":"RSC Adv."},{"key":"14_CR32","unstructured":"Kingma, D.P., Ba, L.J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (2015)"},{"key":"14_CR33","unstructured":"Kipf, T.N., Welling, M.: Variational graph auto-encoders. arXiv preprint arXiv:1611.07308 (2016)"},{"issue":"1","key":"14_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13059-020-02018-y","volume":"21","author":"W Wu","year":"2020","unstructured":"Wu, W., Ji, P., Zhao, F.: 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."},{"issue":"5","key":"14_CR35","doi-asserted-by":"publisher","first-page":"1","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), 1\u20132 (2018)","journal-title":"Cell Death Dis."},{"issue":"3","key":"14_CR36","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1016\/S1470-2045(00)00254-0","volume":"2","author":"TJ Key","year":"2001","unstructured":"Key, T.J., Verkasalo, P.K., Banks, E.: Epidemiology of breast cancer. Lancet Oncol. 2(3), 133\u2013140 (2001)","journal-title":"Lancet Oncol."},{"issue":"9","key":"14_CR37","doi-asserted-by":"publisher","first-page":"494","DOI":"10.1038\/ncpneuro0289","volume":"2","author":"JA Schwartzbaum","year":"2006","unstructured":"Schwartzbaum, J.A., Fisher, J.L., Aldape, K.D., Wrensch, M.: Epidemiology and molecular pathology of glioma. Nat. Clin. Pract. Neurol. 2(9), 494\u2013503 (2006)","journal-title":"Nat. Clin. Pract. Neurol."},{"issue":"7","key":"14_CR38","doi-asserted-by":"publisher","first-page":"896","DOI":"10.1093\/neuonc\/nou087","volume":"16","author":"QT Ostrom","year":"2014","unstructured":"Ostrom, Q.T., et al.: The epidemiology of glioma in adults: a \u201cstate of the science\u201d review. Neuro. Oncol. 16(7), 896\u2013913 (2014)","journal-title":"Neuro. Oncol."}],"container-title":["Lecture Notes in Computer Science","Intelligent Computing Theories and Application"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-13829-4_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T20:04:57Z","timestamp":1710360297000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-13829-4_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031138287","9783031138294"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-13829-4_14","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":"15 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)"}}]}}