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Deep learning tackles single-cell analysis-a survey of deep learning for scRNA-seq analysis.
Flores, Mario; Liu, Zhentao; Zhang, Tinghe; Hasib, Md Musaddaqui; Chiu, Yu-Chiao; Ye, Zhenqing; Paniagua, Karla; Jo, Sumin; Zhang, Jianqiu; Gao, Shou-Jiang; Jin, Yu-Fang; Chen, Yidong; Huang, Yufei.
  • Flores M; Department of Electrical and Computer Engineering, the University of Texas at San Antonio, San Antonio, TX 78249, USA.
  • Liu Z; Department of Electrical and Computer Engineering, the University of Texas at San Antonio, San Antonio, TX 78249, USA.
  • Zhang T; Department of Electrical and Computer Engineering, the University of Texas at San Antonio, San Antonio, TX 78249, USA.
  • Hasib MM; Department of Electrical and Computer Engineering, the University of Texas at San Antonio, San Antonio, TX 78249, USA.
  • Chiu YC; Greehey Children's Cancer Research Institute, University of Texas Health San Antonio, San Antonio, TX 78229, USA.
  • Ye Z; Greehey Children's Cancer Research Institute, University of Texas Health San Antonio, San Antonio, TX 78229, USA.
  • Paniagua K; Department of Population Health Sciences, University of Texas Health San Antonio, San Antonio, TX 78229, USA.
  • Jo S; Department of Electrical and Computer Engineering, the University of Texas at San Antonio, San Antonio, TX 78249, USA.
  • Zhang J; Department of Electrical and Computer Engineering, the University of Texas at San Antonio, San Antonio, TX 78249, USA.
  • Gao SJ; Department of Electrical and Computer Engineering, the University of Texas at San Antonio, San Antonio, TX 78249, USA.
  • Jin YF; Department of Microbiology and Molecular Genetics, University of Pittsburgh, Pittsburgh, Pennsylvania, PA 15232, USA.
  • Chen Y; UPMC Hillman Cancer Center, University of Pittsburgh, PA 15232, USA.
  • Huang Y; Department of Electrical and Computer Engineering, the University of Texas at San Antonio, San Antonio, TX 78249, USA.
Brief Bioinform ; 23(1)2022 01 17.
Article en En | MEDLINE | ID: mdl-34929734
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Análisis de la Célula Individual / Aprendizaje Profundo / RNA-Seq Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Año: 2022 Tipo del documento: Article
Texto completo
Imprimir
XML
PubMed Links
Search on Google

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Análisis de la Célula Individual / Aprendizaje Profundo / RNA-Seq Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Año: 2022 Tipo del documento: Article