Cargando…
Identification of methylation signatures and rules for predicting the severity of SARS-CoV-2 infection with machine learning methods
Patients infected with SARS-CoV-2 at various severities have different clinical manifestations and treatments. Mild or moderate patients usually recover with conventional medical treatment, but severe patients require prompt professional treatment. Thus, stratifying infected patients for targeted tr...
Autores principales: | Liu, Zhiyang, Meng, Mei, Ding, ShiJian, Zhou, XiaoChao, Feng, KaiYan, Huang, Tao, Cai, Yu-Dong |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9537378/ https://www.ncbi.nlm.nih.gov/pubmed/36212830 http://dx.doi.org/10.3389/fmicb.2022.1007295 |
Ejemplares similares
-
Identifying Methylation Signatures and Rules for COVID-19 With Machine Learning Methods
por: Li, Zhandong, et al.
Publicado: (2022) -
Identification of Methylation Signatures and Rules for Sarcoma Subtypes by Machine Learning Methods
por: Ren, Jingxin, et al.
Publicado: (2022) -
Detecting Blood Methylation Signatures in Response to Childhood Cancer Radiotherapy via Machine Learning Methods
por: Li, Zhandong, et al.
Publicado: (2022) -
Detecting Brain Structure-Specific Methylation Signatures and Rules for Alzheimer’s Disease
por: Li, ZhanDong, et al.
Publicado: (2022) -
Identifying Key MicroRNA Signatures for Neurodegenerative Diseases With Machine Learning Methods
por: Li, ZhanDong, et al.
Publicado: (2022)