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dc.contributor.authorOvanessians, Armand
dc.date2023-11
dc.date.accessioned2023-11-19T23:10:07Z
dc.date.available2023-11-19T23:10:07Z
dc.identifier.urihttps://hdl.handle.net/11124/178547
dc.identifier.urihttps://doi.org/10.25676/11124/178547
dc.description.abstractIn the absence of effective antiviral drugs for the treatment of COVID-19, we designed a novel k-partite graph learning method for data matrix completion to uncover hidden viral-host interactions and drug-target interactions. Our framework produces a holistic map of the pathogenesis of COVID-19 and unveils novel FDA approved drug candidates for COVID-19.
dc.format.mediumarticles
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado School of Mines. Arthur Lakes Library
dc.relation.ispartofReuleaux 2023
dc.rightsCreative Commons CC-BY License or the Creative Commons CC-BY-NC License.
dc.sourceContained in: Reuleaux undergraduate research journal: fourth edition, https://hdl.handle.net/11124/178539
dc.titleUnveiling hidden viral host protein interactions in COVID-19 for drug repurposing
dc.typeText
dc.publisher.originalColorado School of Mines. Reuleaux Undergraduate Research Journal


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