Immune responses of different COVID-19 vaccination strategies by analyzing single-cell RNA sequencing data from multiple tissues using machine learning methods.

Li, Hao; Ma, Qinglan; Ren, Jingxin; Guo, Wei; Feng, Kaiyan; Li, Zhandong; Huang, Tao; Cai, Yu-Dong

Li, Hao; Ma, Qinglan; Ren, Jingxin; Guo, Wei; Feng, Kaiyan; Li, Zhandong; Huang, Tao; Cai, Yu-Dong.

Afiliación

Li H; College of Food Engineering, Jilin Engineering Normal University, Changchun, China.
Ma Q; School of Life Sciences, Shanghai University, Shanghai, China.
Ren J; School of Life Sciences, Shanghai University, Shanghai, China.
Guo W; Key Laboratory of Stem Cell Biology, Shanghai Institutes for Biological Sciences (SIBS), Shanghai Jiao Tong University School of Medicine (SJTUSM), Chinese Academy of Sciences (CAS), Shanghai, China.
Feng K; Department of Computer Science, Guangdong AIB Polytechnic College, Guangzhou, China.
Li Z; College of Food Engineering, Jilin Engineering Normal University, Changchun, China.
Huang T; Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
Cai YD; CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.

Front Genet ; 14: 1157305, 2023.

Article en En | MEDLINE | ID: mdl-37007947

Palabras clave

COVID-19 vaccination; SARS-CoV-2 infection; classification rule; immune response; machine learning method

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Front Genet Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza

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