LSH-GAN enables in-silico generation of cells for small sample high dimensional scRNA-seq data
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
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2399-3642
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Anmerkungen
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online resource.
- Erschienen in
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LSH-GAN enables in-silico generation of cells for small sample high dimensional scRNA-seq data ; volume:5 ; number:1 ; day:10 ; month:6 ; year:2022 ; pages:1-9 ; date:12.2022
Communications biology ; 5, Heft 1 (10.6.2022), 1-9, 12.2022
- Urheber
-
Lall, Snehalika
Ray, Sumanta
Bandyopadhyay, Sanghamitra
- Beteiligte Personen und Organisationen
-
SpringerLink (Online service)
- DOI
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10.1038/s42003-022-03473-y
- URN
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urn:nbn:de:101:1-2022080400460219944220
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
15.08.2025, 07:25 MESZ
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Beteiligte
- Lall, Snehalika
- Ray, Sumanta
- Bandyopadhyay, Sanghamitra
- SpringerLink (Online service)
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