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SpCas9 activity prediction by DeepSpCas9, a deep learning–based model with high generalization performance

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dc.contributor.authorHui Kwon Kim-
dc.contributor.authorYounggwang Kim-
dc.contributor.authorSungtae Lee-
dc.contributor.authorSeonwoo Min-
dc.contributor.authorJung Yoon Bae-
dc.contributor.authorJae Woo Choi-
dc.contributor.authorJinman Park-
dc.contributor.authorDongmin Jung-
dc.contributor.authorSungroh Yoon-
dc.contributor.authorHyongbum Henry Kim-
dc.date.available2020-01-31T00:52:17Z-
dc.date.created2019-11-18-
dc.date.issued2019-11-
dc.identifier.issn2375-2548-
dc.identifier.urihttps://pr.ibs.re.kr/handle/8788114/6774-
dc.description.abstractCopyright © 2019 The Authors, some rights reserved;We evaluated SpCas9 activities at 12,832 target sequences using a high-throughput approach based on a human cell library containing single-guide RNA–encoding and target sequence pairs. Deep learning–based training on this large dataset of SpCas9-induced indel frequencies led to the development of a SpCas9 activity–predicting model named DeepSpCas9. When tested against independently generated datasets (our own and those published by other groups), DeepSpCas9 showed high generalization performance. DeepSpCas9 is available at http://deepcrispr.info/DeepSpCas9-
dc.description.uri1-
dc.language영어-
dc.publisherAMER ASSOC ADVANCEMENT SCIENCE-
dc.titleSpCas9 activity prediction by DeepSpCas9, a deep learning–based model with high generalization performance-
dc.typeArticle-
dc.type.rimsART-
dc.identifier.wosid000499736100080-
dc.identifier.scopusid2-s2.0-85074653921-
dc.identifier.rimsid70554-
dc.contributor.affiliatedAuthorHyongbum Henry Kim-
dc.identifier.doi10.1126/sciadv.aax9249-
dc.identifier.bibliographicCitationSCIENCE ADVANCES, v.5, no.11, pp.eaax9249-
dc.citation.titleSCIENCE ADVANCES-
dc.citation.volume5-
dc.citation.number11-
dc.citation.startPageeaax9249-
dc.description.journalClass1-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.subject.keywordPlusSGRNA DESIGN-
dc.subject.keywordPlusHUMAN-CELLS-
dc.subject.keywordPlusGENOME-
dc.subject.keywordPlusSCREENS-
dc.subject.keywordPlusRNAS-
dc.subject.keywordPlusDNA-
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Center for Nanomedicine (나노의학 연구단) > 1. Journal Papers (저널논문)
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