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dc.contributor.authorHan, Wenkai
dc.contributor.authorCao, Huiluo
dc.date.accessioned2020-01-27T08:09:22Z
dc.date.available2020-01-27T08:09:22Z
dc.date.issued2020-1-20
dc.identifier.urihttp://hdl.handle.net/10754/661210
dc.description.abstractAntibiotic Resistance Genes (ARGs) are one of the key components of antibioticresistance, which has become one of the most urgent threats to global health.Here we propose an endto end Hierarchical Multi task Deep learningframework for Antibiotic Resistance Gene annotation (HMD ARG), taking rawsequence encoding as input and then annotating ARGs sequences from threeaspects: resistant drug type, the underlying mechanism of resistance, and genemobility. Experimental results suggest that HMD ARG can serve as a useful toolfor the ARG investigation.
dc.relation.urlhttps://epostersonline.com//dh2020/node/61
dc.titleDeep Learning Enables Rapid Identification of Antibiotic Resistance Genes
dc.typePoster
dc.conference.dateJAN 20 - 22, 2020
dc.conference.nameDigital Health 2020
dc.conference.locationKAUST
dc.contributor.institution
refterms.dateFOA2020-01-27T08:09:22Z


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