{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T20:58:02Z","timestamp":1782853082598,"version":"3.54.5"},"reference-count":144,"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\/501100003725","name":"Ministry of Science and ICT (MSIT), Korea Government","doi-asserted-by":"publisher","award":["NRF-2019R1C1C1006143"],"award-info":[{"award-number":["NRF-2019R1C1C1006143"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"Ministry of Science and ICT (MSIT), Korea Government","doi-asserted-by":"publisher","award":["NRF-2019R1I1A3A01060518"],"award-info":[{"award-number":["NRF-2019R1I1A3A01060518"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Institute of Information & Communications Technology Planning & Evaluation"},{"DOI":"10.13039\/501100002543","name":"Korea Government (MSIT) through the Artificial Intelligence Convergence Research Center, Pusan National University","doi-asserted-by":"publisher","award":["2020-0-01450"],"award-info":[{"award-number":["2020-0-01450"]}],"id":[{"id":"10.13039\/501100002543","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.2020.3009328","type":"journal-article","created":{"date-parts":[[2020,7,15]],"date-time":"2020-07-15T16:57:31Z","timestamp":1594832251000},"page":"130820-130839","source":"Crossref","is-referenced-by-count":239,"title":["Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts"],"prefix":"10.1109","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9485-9216","authenticated-orcid":false,"given":"Quoc-Viet","family":"Pham","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8092-6756","authenticated-orcid":false,"given":"Dinh C.","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9172-2935","authenticated-orcid":false,"given":"Thien","family":"Huynh-The","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8398-564X","authenticated-orcid":false,"given":"Won-Joo","family":"Hwang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8014-7798","authenticated-orcid":false,"given":"Pubudu N.","family":"Pathirana","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1017\/ice.2020.61"},{"key":"ref38","article-title":"A novel AI-enabled framework to diagnose coronavirus COVID 19 using smartphone embedded sensors: Design study","author":"maghdid","year":"2020","journal-title":"arXiv 2003 07434"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.2196\/19540"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/S2589-7500(20)30059-5"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1038\/d41586-018-02473-5"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1093\/infdis\/jiw400"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.2807\/1560-7917.ES.2020.25.14.2000398"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.2807\/1560-7917.ES.2020.25.3.2000045"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2870052"},{"key":"ref34","first-page":"201","article-title":"Estimation of the reproductive number of novel coronavirus (COVID-19) and the probable outbreak size on the diamond princess cruise ship: A data-driven analysis","volume":"93","author":"zhang","year":"2020","journal-title":"The Journal of Infectious Diseases"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-49736-5_7"},{"key":"ref27","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1007\/978-3-319-55774-8_10","article-title":"Big data analytics in healthcare Internet of Things","author":"manogaran","year":"2017","journal-title":"Innovative healthcare systems for the 21st century"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph15081596"},{"key":"ref20","first-page":"5865","article-title":"A survey on big data analytics in health care","volume":"5","author":"priyanka","year":"2014","journal-title":"Int J Comput Sci Inf Technol"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2971576"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2018.03.013"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2970143"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2953499"},{"key":"ref101","article-title":"COVID-19 epidemic outcome predictions based on logistic fitting and estimation of its reliability","author":"t\u00e1trai","year":"2020","journal-title":"arXiv 2003 14160"},{"key":"ref26","year":"2020","journal-title":"Almost One Zettabyte of Mobile Data Traffic in 2022-Cisco"},{"key":"ref100","article-title":"Epidemic analysis of COVID-19 in China by dynamical modeling","author":"peng","year":"2020","journal-title":"arXiv 2002 06563"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2971576"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1016\/j.mehy.2020.109761"},{"key":"ref51","article-title":"Estimating uncertainty and interpretability in deep learning for coronavirus (COVID-19) detection","author":"ghoshal","year":"2020","journal-title":"arXiv 2003 10769"},{"key":"ref59","article-title":"Quantification of tomographic patterns associated with COVID-19 from chest CT","author":"chaganti","year":"2020","journal-title":"arXiv 2004 01279"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1177\/0846537120913033"},{"key":"ref57","article-title":"Coronavirus (COVID-19) classification using deep features fusion and ranking technique","author":"ozkaya","year":"2020","journal-title":"arXiv 2004 03698"},{"key":"ref56","article-title":"A deep learning algorithm using CT images to screen for corona virus disease (COVID-19)","author":"wang","year":"2020","journal-title":"medRxiv"},{"key":"ref55","article-title":"Rapid AI development cycle for the coronavirus (COVID-19) pandemic: Initial results for automated detection & patient monitoring using deep learning CT image analysis","author":"gozes","year":"2020","journal-title":"arXiv 2003 05037"},{"key":"ref54","article-title":"COVID-Net: A tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images","author":"wang","year":"2020","journal-title":"arXiv 2003 09871"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-bioeng-071516-044442"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.07.005"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2015.06.003"},{"key":"ref4","year":"2020","journal-title":"arXiv Announces New COVID-19 Quick Search"},{"key":"ref3","year":"2020","journal-title":"White House Announces New Partnership to Unleash U S Supercomputing Resources to Fight COVID-19"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijantimicag.2020.105951"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijsu.2020.02.034"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.tre.2020.101922"},{"key":"ref49","article-title":"Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks","author":"narin","year":"2020","journal-title":"arXiv 2003 10849"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1080\/00207543.2020.1750727"},{"key":"ref9","author":"goodfellow","year":"2016","journal-title":"Deep Learning"},{"key":"ref46","article-title":"Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network","author":"abbas","year":"2020","journal-title":"arXiv 2003 13815"},{"key":"ref45","article-title":"Detection of coronavirus (COVID-19) associated pneumonia based on generative adversarial networks and a fine-tuned deep transfer learning model using chest X-ray dataset","author":"eldeen m khalifa","year":"2020","journal-title":"arXiv 2004 01184"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1007\/s13246-020-00865-4"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.103792"},{"key":"ref42","article-title":"Coronavirus detection and analysis on chest CT with deep learning","author":"gozes","year":"2020","journal-title":"arXiv 2004 02640"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3001277"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2020.109944"},{"key":"ref43","article-title":"Coronavirus (COVID-19) classification using CT images by machine learning methods","author":"barstugan","year":"2020","journal-title":"arXiv 2003 09424"},{"key":"ref127","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-019-0956-2"},{"key":"ref126","year":"2020","journal-title":"Cisco Annual Internet Report (2018–2023)"},{"key":"ref125","doi-asserted-by":"publisher","DOI":"10.3390\/s17112449"},{"key":"ref124","doi-asserted-by":"publisher","DOI":"10.1021\/acsnano.0c02624"},{"key":"ref73","article-title":"Analysis of the COVID-19 pandemic by SIR model and machine learning technics for forecasting","author":"ndiaye","year":"2020","journal-title":"arXiv 2004 01574"},{"key":"ref72","article-title":"Modelling death rates due to COVID-19: A Bayesian approach","author":"bayes","year":"2020","journal-title":"arXiv 2004 02386"},{"key":"ref129","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkv404"},{"key":"ref71","article-title":"Forecasting the worldwide spread of COVID-19 based on logistic model and SEIR model","author":"zhou","year":"2020","journal-title":"medRxiv"},{"key":"ref128","article-title":"Potential neutralizing antibodies discovered for novel corona virus using machine learning","author":"magar","year":"2020","journal-title":"arXiv 2003 08447"},{"key":"ref70","article-title":"Neural network aided quarantine control model estimation of global COVID-19 spread","author":"dandekar","year":"2020","journal-title":"arXiv 2004 02752"},{"key":"ref76","article-title":"Forecasting and evaluating intervention of COVID-19 in the world","author":"hu","year":"2020","journal-title":"arXiv 2003 09800"},{"key":"ref130","first-page":"611","author":"offermanns","year":"2008","journal-title":"IC50 Values"},{"key":"ref77","article-title":"Neural network based country wise risk prediction of COVID-19","author":"pal","year":"2020","journal-title":"arXiv 2004 00959"},{"key":"ref74","article-title":"An ARIMA model to forecast the spread and the final size of COVID-2019 epidemic in Italy","author":"perone","year":"2020","journal-title":"arXiv 2004 00382"},{"key":"ref75","article-title":"Machine learning the phenomenology of COVID-19 from early infection dynamics","author":"magdon-ismail","year":"2020","journal-title":"arXiv 2003 07602"},{"key":"ref133","year":"2020","journal-title":"Seoul Introduces the COVID-19 AI Monitoring Call System"},{"key":"ref134","year":"2020","journal-title":"How Next-Generation Information Technologies Tackled COVID-19 in China"},{"key":"ref131","doi-asserted-by":"publisher","DOI":"10.1201\/9780203911327.ch14"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-35139-7_7"},{"key":"ref132","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijantimicag.2020.105924"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.3551338"},{"key":"ref136","article-title":"COVID-19: A survey on public medical imaging data resources","author":"kalkreuth","year":"2020","journal-title":"arXiv 2004 04569"},{"key":"ref135","year":"2020","journal-title":"How DAMO Academy’s AI System Detects Coronavirus Cases"},{"key":"ref138","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocx068"},{"key":"ref137","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2020.102693"},{"key":"ref60","article-title":"Lung infection quantification of COVID-19 in CT images with deep learning","author":"shan","year":"2020","journal-title":"arXiv 2003 04655"},{"key":"ref139","year":"2020","journal-title":"Mipasa Project and ibm Blockchain Team on Open Data Platform to Support Covid-19 Response"},{"key":"ref62","article-title":"Severity assessment of coronavirus disease 2019 (COVID-19) using quantitative features from chest CT images","author":"tang","year":"2020","journal-title":"arXiv 2003 11988"},{"key":"ref61","first-page":"630","article-title":"Identity mappings in deep residual networks","author":"he","year":"2016","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2020.100378"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2972577"},{"key":"ref140","doi-asserted-by":"publisher","DOI":"10.1145\/3298981"},{"key":"ref65","article-title":"A time-dependent SIR model for COVID-19 with undetectable infected persons","author":"chen","year":"2020","journal-title":"arXiv 2003 00122"},{"key":"ref141","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2014.2387836"},{"key":"ref66","article-title":"Space-time dependence of corona virus (COVID-19) outbreak","author":"biswas","year":"2020","journal-title":"arXiv 2003 03149"},{"key":"ref142","author":"rees","year":"2020","journal-title":"AI and Cloud Computing Used to Develop COVID-19 Vaccine"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1142\/S0129183120501351"},{"key":"ref143","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2992341"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.3934\/mine.2021013"},{"key":"ref144","doi-asserted-by":"publisher","DOI":"10.1016\/S1473-3099(20)30190-0"},{"key":"ref2","year":"2020","journal-title":"Coronavirus (COVID-19)"},{"key":"ref69","article-title":"Neural network aided quarantine control model estimation of COVID spread in Wuhan, China","author":"dandekar","year":"2020","journal-title":"arXiv 2003 09403"},{"key":"ref1","year":"2020","journal-title":"Coronavirus disease (COVID-19) Pandemic"},{"key":"ref109","doi-asserted-by":"publisher","DOI":"10.1142\/S012918312050103X"},{"key":"ref95","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-019-1923-7"},{"key":"ref108","doi-asserted-by":"publisher","DOI":"10.1016\/j.geosus.2020.03.005"},{"key":"ref94","article-title":"Computational predictions of protein structures associated with COVID-19","author":"jumper","year":"2020"},{"key":"ref107","article-title":"Tracking the spread of novel coronavirus (2019-nCoV) based on big data","author":"zhao","year":"2020","journal-title":"medRxiv"},{"key":"ref93","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2020.01581"},{"key":"ref106","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-020-0832-5"},{"key":"ref92","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.3561442"},{"key":"ref105","article-title":"Metropolitan-scale COVID-19 outbreaks: How similar are they?","author":"heroy","year":"2020","journal-title":"arXiv 2004 01248"},{"key":"ref91","doi-asserted-by":"publisher","DOI":"10.1002\/minf.202000028"},{"key":"ref104","article-title":"SEIR and regression model based COVID-19 outbreak predictions in India","author":"gupta","year":"2020","journal-title":"medRxiv"},{"key":"ref90","article-title":"One-shot screening of potential peptide ligands on HR1 domain in COVID-19 glycosylated spike (S) protein with deep siamese network","author":"savioli","year":"2020","journal-title":"arXiv 2004 02136"},{"key":"ref103","article-title":"Quantitative assessment of the role of undocumented infection in the 2019 novel coronavirus (COVID-19) pandemic","author":"long","year":"2020","journal-title":"arXiv 2003 12028"},{"key":"ref102","doi-asserted-by":"publisher","DOI":"10.1016\/j.bbi.2020.04.042"},{"key":"ref111","article-title":"Tracking COVID-19 using online search","author":"lampos","year":"2020","journal-title":"arXiv 2003 08086"},{"key":"ref112","doi-asserted-by":"publisher","DOI":"10.1007\/s13347-017-0278-y"},{"key":"ref110","article-title":"Temperature dependence of COVID-19 transmission","author":"notari","year":"2020","journal-title":"arXiv 2003 12417"},{"key":"ref98","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4614-1686-9"},{"key":"ref99","article-title":"Visual data analysis and simulation prediction for COVID-19","author":"chen","year":"2020","journal-title":"arXiv 2002 07096"},{"key":"ref96","article-title":"Fast and flexible design of novel proteins using graph neural networks","author":"strokach","year":"2020","journal-title":"BioRxiv"},{"key":"ref97","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-020-0883-7"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/LCOMM.2020.2968030"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph17093176"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.csbj.2020.03.025"},{"key":"ref13","first-page":"1e","article-title":"Potential 2019-nCoV 3C-like protease inhibitors designed using generative deep learning approaches","volume":"307","author":"zhavoronkov","year":"2020","journal-title":"Insilico Med Hong Kong Ltd A"},{"key":"ref14","article-title":"Deep learning-based detection for COVID-19 from chest CT using weak label","author":"zheng","year":"2020","journal-title":"medRxiv"},{"key":"ref15","article-title":"Artificial intelligence forecasting of COVID-19 in China","author":"hu","year":"2020","journal-title":"arXiv 2002 07112"},{"key":"ref118","doi-asserted-by":"publisher","DOI":"10.1186\/s40779-020-0233-6"},{"key":"ref16","year":"2020","journal-title":"COVID-19 Open Research Dataset Challenge (CORD-19) An AI Challenge With AI2 CZI MSR Georgetown NIH & the White House"},{"key":"ref82","article-title":"A machine learning methodology for real-time forecasting of the 2019–2020 COVID-19 outbreak using Internet searches, news alerts, and estimates from mechanistic models","author":"liu","year":"2020","journal-title":"arXiv 2004 04019"},{"key":"ref117","article-title":"Prediction and evolution of B cell epitopes of surface protein in SARS-CoV-2","author":"lon","year":"2020","journal-title":"BioRxiv"},{"key":"ref17","year":"2020","journal-title":"IBM Releases Novel AI-Powered Technologies to Help Health and Research Community Accelerate the Discovery of Medical Insights and Treatments for COVID-19"},{"key":"ref81","article-title":"$\\alpha$\n-satellite: An AI-driven system and benchmark datasets for hierarchical community-level risk assessment to help combat COVID-19","author":"ye","year":"2020","journal-title":"arXiv 2003 12232"},{"key":"ref18","article-title":"A visualized botnet detection system based deep learning for the Internet of Things networks of smart cities","author":"alazab","year":"2020","journal-title":"IEEE Trans Ind Appl"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1021\/acs.molpharmaceut.5b00982"},{"key":"ref119","doi-asserted-by":"publisher","DOI":"10.3390\/v12030254"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-015-0030-3"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1126\/science.aba9757"},{"key":"ref114","doi-asserted-by":"publisher","DOI":"10.1093\/ve\/veaa027"},{"key":"ref113","article-title":"High sensitivity detection of SARS-CoV-2 using multiplex PCR and a multiplex-PCR-based metagenomic method","author":"li","year":"2020","journal-title":"BioRxiv"},{"key":"ref116","article-title":"Non-neural expression of SARS-CoV-2 entry genes in the olfactory epithelium suggests mechanisms underlying anosmia in COVID-19 patients","author":"brann","year":"2020","journal-title":"BioRxiv"},{"key":"ref80","article-title":"COVID-19 and computer audition: An overview on what speech & sound analysis could contribute in the SARS-CoV-2 corona crisis","author":"schuller","year":"2020","journal-title":"arXiv 2003 11117"},{"key":"ref115","article-title":"Re-analysis of SARS-CoV-2 infected host cell proteomics time-course data by impact pathway analysis and network analysis. A potential link with inflammatory response","author":"ortea","year":"2020","journal-title":"BioRxiv"},{"key":"ref120","article-title":"Energetics based epitope screening in SARS CoV-2 (COVID 19) spike glycoprotein by immuno-informatic analysis aiming to a suitable vaccine development","author":"banerjee","year":"2020","journal-title":"BioRxiv"},{"key":"ref89","article-title":"CogMol: Target-specific and selective drug design for COVID-19 using deep generative models","author":"chenthamarakshan","year":"2020","journal-title":"arXiv 2004 01215"},{"key":"ref121","article-title":"The essential facts of Wuhan novel coronavirus outbreak in China and epitope-based vaccine designing against 2019-nCoV","author":"sarkar","year":"2020","journal-title":"BioRxiv"},{"key":"ref122","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/2683286"},{"key":"ref123","article-title":"FEP-based screening prompts drug repositioning against COVID-19","author":"li","year":"2020","journal-title":"BioRxiv"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.1016\/j.gpb.2017.07.003"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.1038\/s41563-019-0338-z"},{"key":"ref87","article-title":"Prediction of potential commercially inhibitors against SARS-CoV-2 by multi-task deep model","author":"hu","year":"2020","journal-title":"arXiv 2003 00728"},{"key":"ref88","article-title":"A data-driven drug repositioning framework discovered a potential therapeutic agent targeting COVID-19","author":"ge","year":"2020","journal-title":"BioRxiv"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8948470\/09141265.pdf?arnumber=9141265","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T10:57:40Z","timestamp":1641985060000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9141265\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":144,"URL":"https:\/\/doi.org\/10.1109\/access.2020.3009328","relation":{"has-preprint":[{"id-type":"doi","id":"10.20944\/preprints202004.0383.v1","asserted-by":"object"}]},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]}}}