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Identifying Methylation Signatures and Rules for COVID-19 With Machine Learning Methods.
Li, Zhandong; Mei, Zi; Ding, Shijian; Chen, Lei; Li, Hao; Feng, Kaiyan; Huang, Tao; Cai, Yu-Dong.
  • Li Z; College of Biological and Food Engineering, Jilin Engineering Normal University, Changchun, China.
  • Mei Z; Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, China.
  • Ding S; School of Life Sciences, Shanghai University, Shanghai, China.
  • Chen L; College of Information Engineering, Shanghai Maritime University, Shanghai, China.
  • Li H; College of Biological and Food Engineering, Jilin Engineering Normal University, Changchun, 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 Mol Biosci ; 9: 908080, 2022.
Article in English | MEDLINE | ID: covidwho-1952442
ABSTRACT
The occurrence of coronavirus disease 2019 (COVID-19) has become a serious challenge to global public health. Definitive and effective treatments for COVID-19 are still lacking, and targeted antiviral drugs are not available. In addition, viruses can regulate host innate immunity and antiviral processes through the epigenome to promote viral self-replication and disease progression. In this study, we first analyzed the methylation dataset of COVID-19 using the Monte Carlo feature selection method to obtain a feature list. This feature list was subjected to the incremental feature selection method combined with a decision tree algorithm to extract key biomarkers, build effective classification models and classification rules that can remarkably distinguish patients with or without COVID-19. EPSTI1, NACAP1, SHROOM3, C19ORF35, and MX1 as the essential features play important roles in the infection and immune response to novel coronavirus. The six significant rules extracted from the optimal classifier quantitatively explained the expression pattern of COVID-19. Therefore, these findings validated that our method can distinguish COVID-19 at the methylation level and provide guidance for the diagnosis and treatment of COVID-19.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Journal: Front Mol Biosci Year: 2022 Document Type: Article Affiliation country: Fmolb.2022.908080

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Journal: Front Mol Biosci Year: 2022 Document Type: Article Affiliation country: Fmolb.2022.908080