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Feasibility Study of Deep Learning Based Radiosensitivity Binary Classification Model Using Gene Expression Profiling

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dc.contributor.authorKim, Euidam-
dc.contributor.authorChung, Yoonsun-
dc.date.accessioned2021-12-27T10:25:42Z-
dc.date.available2021-12-27T10:25:42Z-
dc.date.created2021-11-25-
dc.date.issued2021-07-
dc.identifier.issn0094-2405-
dc.identifier.urihttps://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/133913-
dc.language영어-
dc.language.isoen-
dc.publisherWILEY-
dc.titleFeasibility Study of Deep Learning Based Radiosensitivity Binary Classification Model Using Gene Expression Profiling-
dc.typeConference-
dc.contributor.affiliatedAuthorChung, Yoonsun-
dc.identifier.wosid000673145403140-
dc.identifier.bibliographicCitationThe 2021 AAPM Virtual Annual Meeting-
dc.relation.isPartOfThe 2021 AAPM Virtual Annual Meeting-
dc.relation.isPartOfMEDICAL PHYSICS-
dc.citation.titleThe 2021 AAPM Virtual Annual Meeting-
dc.citation.conferencePlaceUS-
dc.citation.conferenceDate2021-07-25-
dc.type.rimsCONF-
dc.description.journalClass1-
dc.identifier.urlhttps://www.researchgate.net/publication/351744132_Feasibility_Study_of_Deep_Learning_Based_Radiosensitivity_Binary_Classification_Model_Using_Gene_Expression_Profiling-
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