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scGraph: a graph neural network-based approach to automatically identify cell types.
Yin, Qijin; Liu, Qiao; Fu, Zhuoran; Zeng, Wanwen; Zhang, Boheng; Zhang, Xuegong; Jiang, Rui; Lv, Hairong.
Afiliación
  • Yin Q; Ministry of Education Key Laboratory of Bioinformatics, Research Department of Bioinformatics at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing 100084, China.
  • Liu Q; Department of Statistics, Stanford University, Stanford, CA 94305, USA.
  • Fu Z; Ministry of Education Key Laboratory of Bioinformatics, Research Department of Bioinformatics at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing 100084, China.
  • Zeng W; Department of Statistics, Stanford University, Stanford, CA 94305, USA.
  • Zhang B; College of Software, Nankai University, Tianjin 300350, China.
  • Zhang X; Ministry of Education Key Laboratory of Bioinformatics, Research Department of Bioinformatics at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing 100084, China.
  • Jiang R; Ministry of Education Key Laboratory of Bioinformatics, Research Department of Bioinformatics at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing 100084, China.
  • Lv H; Ministry of Education Key Laboratory of Bioinformatics, Research Department of Bioinformatics at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing 100084, China.
Bioinformatics ; 38(11): 2996-3003, 2022 05 26.
Article en En | MEDLINE | ID: mdl-35394015
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Perfilación de la Expresión Génica / Análisis de la Célula Individual Tipo de estudio: Prognostic_studies Idioma: En Revista: Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2022 Tipo del documento: Article País de afiliación: China
Texto completo
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Perfilación de la Expresión Génica / Análisis de la Célula Individual Tipo de estudio: Prognostic_studies Idioma: En Revista: Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2022 Tipo del documento: Article País de afiliación: China