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NormAE: Deep Adversarial Learning Model to Remove Batch Effects in Liquid Chromatography Mass Spectrometry-Based Metabolomics Data.
Rong, Zhiwei; Tan, Qilong; Cao, Lei; Zhang, Liuchao; Deng, Kui; Huang, Yue; Zhu, Zheng-Jiang; Li, Zhenzi; Li, Kang.
Afiliación
  • Rong Z; Department of Epidemiology and Biostatistics, School of Public Health, Harbin Medical University, Harbin 150086, China.
  • Tan Q; Department of Epidemiology and Biostatistics, School of Public Health, Harbin Medical University, Harbin 150086, China.
  • Cao L; Department of Epidemiology and Biostatistics, School of Public Health, Harbin Medical University, Harbin 150086, China.
  • Zhang L; Department of Epidemiology and Biostatistics, School of Public Health, Harbin Medical University, Harbin 150086, China.
  • Deng K; Department of Epidemiology and Biostatistics, School of Public Health, Harbin Medical University, Harbin 150086, China.
  • Huang Y; Department of Epidemiology and Biostatistics, School of Public Health, Harbin Medical University, Harbin 150086, China.
  • Zhu ZJ; Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 200032, China.
  • Li Z; Department of Epidemiology and Biostatistics, School of Public Health, Harbin Medical University, Harbin 150086, China.
  • Li K; Department of Epidemiology and Biostatistics, School of Public Health, Harbin Medical University, Harbin 150086, China.
Anal Chem ; 92(7): 5082-5090, 2020 04 07.
Article en En | MEDLINE | ID: mdl-32207605
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Metabolómica / Aprendizaje Profundo Tipo de estudio: Prognostic_studies Idioma: En Revista: Anal Chem Año: 2020 Tipo del documento: Article País de afiliación: China
Texto completo
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Metabolómica / Aprendizaje Profundo Tipo de estudio: Prognostic_studies Idioma: En Revista: Anal Chem Año: 2020 Tipo del documento: Article País de afiliación: China