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dc.contributor.authorYoung, Richard
dc.date.accessioned2023-01-09T16:51:33Z
dc.date.available2023-01-09T16:51:33Z
dc.date.issued2021
dc.identifier.urihttps://hdl.handle.net/1721.1/147014
dc.description.abstract<jats:p>The CaCTS algorithm nominates cancer cell master transcription factors and guides a model of ovarian cancer regulatory circuitry.</jats:p>en_US
dc.language.isoen
dc.publisherAmerican Association for the Advancement of Science (AAAS)en_US
dc.relation.isversionof10.1126/SCIADV.ABF6123en_US
dc.rightsCreative Commons Attribution NonCommercial License 4.0en_US
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/en_US
dc.sourceScience Advancesen_US
dc.titlePredicting master transcription factors from pan-cancer expression dataen_US
dc.typeArticleen_US
dc.identifier.citationYoung, Richard. 2021. "Predicting master transcription factors from pan-cancer expression data." Science Advances, 7 (48).
dc.contributor.departmentMassachusetts Institute of Technology. Department of Biologyen_US
dc.relation.journalScience Advancesen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2023-01-09T16:47:52Z
dspace.orderedauthorsReddy, J; Fonseca, MAS; Corona, RI; Nameki, R; Segato Dezem, F; Klein, IA; Chang, H; Chaves-Moreira, D; Afeyan, LK; Malta, TM; Lin, X; Abbasi, F; Font-Tello, A; Sabedot, T; Cejas, P; Rodríguez-Malavé, N; Seo, J-H; Lin, D-C; Matulonis, U; Karlan, BY; Gayther, SA; Pasaniuc, B; Gusev, A; Noushmehr, H; Long, H; Freedman, ML; Drapkin, R; Young, RA; Abraham, BJ; Lawrenson, Ken_US
dspace.date.submission2023-01-09T16:47:58Z
mit.journal.volume7en_US
mit.journal.issue48en_US
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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