Identification of methylation signatures and rules for predicting the severity of SARS-CoV-2 infection with machine learning methods.

Liu, Zhiyang; Meng, Mei; Ding, ShiJian; Zhou, XiaoChao; Feng, KaiYan; Huang, Tao; Cai, Yu-Dong

Liu, Zhiyang; Meng, Mei; Ding, ShiJian; Zhou, XiaoChao; Feng, KaiYan; Huang, Tao; Cai, Yu-Dong.

Afiliación

Liu Z; School of Life Sciences, Changchun Sci-Tech University, Changchun, China.
Meng M; State Key Laboratory of Oncogenes and Related Genes, Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Ding S; School of Life Sciences, Shanghai University, Shanghai, China.
Zhou X; State Key Laboratory of Oncogenes and Related Genes, Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Feng K; Department of Computer Science, Guangdong AIB Polytechnic College, Guangzhou, 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 Microbiol ; 13: 1007295, 2022.

Article en En | MEDLINE | ID: mdl-36212830

Palabras clave

SARS-CoV-2; classification rule; machine learning; methylation; severity

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Front Microbiol Año: 2022 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza

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