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A joint deep learning model enables simultaneous batch effect correction, denoising, and clustering in single-cell transcriptomics.
Lakkis, Justin; Wang, David; Zhang, Yuanchao; Hu, Gang; Wang, Kui; Pan, Huize; Ungar, Lyle; Reilly, Muredach P; Li, Xiangjie; Li, Mingyao.
Afiliação
  • Lakkis J; Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA.
  • Wang D; Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA.
  • Zhang Y; Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA.
  • Hu G; School of Statistics and Data Science, Key Laboratory for Medical Data Analysis and Statistical Research of Tianjin, Nankai University, Tianjin 300071, China.
  • Wang K; Department of Information Theory and Data Science, School of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, China.
  • Pan H; Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, New York 10032, USA.
  • Ungar L; Department of Computer and Information Science, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA.
  • Reilly MP; Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, New York 10032, USA.
  • Li X; School of Statistics and Data Science, Key Laboratory for Medical Data Analysis and Statistical Research of Tianjin, Nankai University, Tianjin 300071, China.
  • Li M; Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA.
Genome Res ; 31(10): 1753-1766, 2021 10.
Article em En | MEDLINE | ID: mdl-34035047
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Transcriptoma / Aprendizado Profundo Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article
Texto completo
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XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Transcriptoma / Aprendizado Profundo Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article