{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T01:12:38Z","timestamp":1768525958728,"version":"3.49.0"},"reference-count":86,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003621","name":"Ministry of Science, ICT, and Future Planning, through the Basic Science Research Program","doi-asserted-by":"publisher","award":["NRF-2019K2A9A2A06020672"],"award-info":[{"award-number":["NRF-2019K2A9A2A06020672"]}],"id":[{"id":"10.13039\/501100003621","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003621","name":"Ministry of Science, ICT, and Future Planning, through the Basic Science Research Program","doi-asserted-by":"publisher","award":["2020R1A2B5B02001717"],"award-info":[{"award-number":["2020R1A2B5B02001717"]}],"id":[{"id":"10.13039\/501100003621","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2020]]},"DOI":"10.1109\/access.2020.3039624","type":"journal-article","created":{"date-parts":[[2020,11,20]],"date-time":"2020-11-20T20:34:55Z","timestamp":1605904495000},"page":"210800-210815","source":"Crossref","is-referenced-by-count":9,"title":["Class-Incremental Learning With Deep Generative Feature Replay for DNA Methylation-Based Cancer Classification"],"prefix":"10.1109","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9724-8955","authenticated-orcid":false,"given":"Erdenebileg","family":"Batbaatar","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7133-3051","authenticated-orcid":false,"given":"Kwang Ho","family":"Park","sequence":"additional","affiliation":[]},{"given":"Tsatsral","family":"Amarbayasgalan","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9375-7621","authenticated-orcid":false,"given":"Khishigsuren","family":"Davagdorj","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6740-219X","authenticated-orcid":false,"given":"Lkhagvadorj","family":"Munkhdalai","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6513-4216","authenticated-orcid":false,"given":"Van-Huy","family":"Pham","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0394-9054","authenticated-orcid":false,"given":"Keun Ho","family":"Ryu","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1038\/nature05913"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMra072067"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1093\/carcin\/bgp220"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1200\/JCO.2004.07.151"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1038\/ncponc0354"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-12-380866-0.60002-2"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.2217\/epi.12.21"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-11-587"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.2217\/epi.11.105"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.3390\/biology5010003"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btw785"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.3892\/or.2019.7151"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0061318"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0186906"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.celrep.2018.03.046"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-014-0007-7"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1021\/acs.molpharmaceut.6b00248"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2788044"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1038\/srep26286"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.phrp.2014.08.004"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2016.11.008"},{"key":"ref22"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.3390\/sym12010154"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbw068"},{"key":"ref25","article-title":"Auto-encoding variational Bayes","author":"Kingma","year":"2013","journal-title":"arXiv:1312.6114"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1142\/9789813235533_0008"},{"key":"ref27","article-title":"An unsupervised deep learning framework with variational autoencoders for genome-wide DNA methylation analysis and biologic feature extraction applied to breast cancer","author":"Titus","year":"2018","journal-title":"BioRxiv"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/BIBM.2018.8621365"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1186\/s12859-019-3130-9"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1186\/s12859-020-3443-8"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1186\/s12859-020-3516-8"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21551"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1126\/science.1659743"},{"key":"ref34","first-page":"1033","article-title":"HLH Across Speciality Collaboration","volume-title":"COVID-19: Consider Cytokine Storm Syndromes and Immunosuppression","volume":"395","author":"Mehta","year":"2019"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1038\/nature01254"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.753"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.148"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.2200\/S00832ED1V01Y201802AIM037"},{"key":"ref39","article-title":"Efficient lifelong learning with A-GEM","author":"Chaudhry","year":"2018","journal-title":"arXiv:1812.00420"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2019.01.012"},{"key":"ref41","article-title":"A continual learning survey: Defying forgetting in classification tasks","author":"De Lange","year":"2019","journal-title":"arXiv:1909.08383"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.587"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00067"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/WACV45572.2020.9093365"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2916503"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1611835114"},{"key":"ref47","first-page":"2672","article-title":"Generative adversarial nets","volume-title":"Adv. Neural Inf. Process. Syst.","author":"Goodfellow"},{"key":"ref48","first-page":"2990","article-title":"Continual learning with deep generative replay","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Shin"},{"key":"ref49","article-title":"Variational continual learning","author":"Nguyen","year":"2017","journal-title":"arXiv:1710.10628"},{"key":"ref50","article-title":"Continual learning in generative adversarial nets","author":"Seff","year":"2017","journal-title":"arXiv:1705.08395"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-54472-4_67"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2017.08.040"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1038\/nature12634"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1003503"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkw124"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.18632\/aging.100908"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-32-9990-0_12"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/17.suppl_1.S157"},{"key":"ref59","article-title":"Distilling the knowledge in a neural network","author":"Hinton","year":"2015","journal-title":"arXiv:1503.02531"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2773081"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2019.03.010"},{"key":"ref62","article-title":"Progress & Compress: A scalable framework for continual learning","author":"Schwarz","year":"2018","journal-title":"arXiv:1805.06370"},{"key":"ref63","first-page":"3987","article-title":"Continual learning through synaptic intelligence","volume":"70","author":"Zenke","year":"2017","journal-title":"Proc. Mach. Learn. Res."},{"key":"ref64","article-title":"PathNet: Evolution channels gradient descent in super neural networks","author":"Fernando","year":"2017","journal-title":"arXiv:1701.08734"},{"key":"ref65","article-title":"Progressive neural networks","author":"Rusu","year":"2016","journal-title":"arXiv:1606.04671"},{"key":"ref66","first-page":"11849","article-title":"Online continual learning with maximal interfered retrieval","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Aljundi"},{"key":"ref67","first-page":"151","article-title":"DeeSIL: Deep-shallow incremental learning","volume-title":"Proc. Eur. Conf. Comput. Vis. (ECCV)","author":"Belouadah"},{"key":"ref68","article-title":"Online learned continual compression with adaptive quantization modules","author":"Caccia","year":"2019","journal-title":"arXiv:1911.08019"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00092"},{"key":"ref70","article-title":"Continual reinforcement learning deployed in real-life using policy distillation and Sim2Real transfer","author":"Traor\u00e9","year":"2019","journal-title":"arXiv:1906.04452"},{"key":"ref71","article-title":"Partitioned variational inference: A unified framework encompassing federated and continual learning","author":"Bui","year":"2018","journal-title":"arXiv:1811.11206"},{"key":"ref72","article-title":"DisCoRL: Continual reinforcement learning via policy distillation","author":"Traor\u00e9","year":"2019","journal-title":"arXiv:1907.05855"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00046"},{"key":"ref74","first-page":"350","article-title":"Experience replay for continual learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Rolnick"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2019.8851986"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-30484-3_38"},{"key":"ref77","first-page":"5962","article-title":"Memory replay GANs: Learning to generate new categories without forgetting","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Wu"},{"key":"ref78","article-title":"A strategy for an uncompromising incremental learner","author":"Venkatesan","year":"2017","journal-title":"arXiv:1705.00744"},{"key":"ref79","article-title":"Generative replay with feedback connections as a general strategy for continual learning","author":"van de Ven","year":"2018","journal-title":"arXiv:1809.10635"},{"key":"ref80","article-title":"Regularization shortcomings for continual learning","author":"Lesort","year":"2019","journal-title":"arXiv:1912.03049"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1038\/s41587-020-0546-8"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gng002"},{"key":"ref83","first-page":"6467","article-title":"Gradient episodic memory for continual learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Lopez-Paz"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33709-3_35"},{"key":"ref85","article-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2014","journal-title":"arXiv:1412.6980"},{"key":"ref86","article-title":"Three scenarios for continual learning","author":"van de Ven","year":"2019","journal-title":"arXiv:1904.07734"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8948470\/09265181.pdf?arnumber=9265181","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,24]],"date-time":"2024-01-24T01:14:52Z","timestamp":1706058892000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9265181\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":86,"URL":"https:\/\/doi.org\/10.1109\/access.2020.3039624","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]}}}