| Identifying Methylation Signatures and Rules for COVID-19 With Machine Learning Methods |
| 論文作者 |
Li, ZD; Mei, Z; Ding, SJ; Chen, L; Li, H; Feng, KY; Huang, T; Cai, YD |
| 期刊/會議名稱 |
FRONTIERS IN MOLECULAR BIOSCIENCES |
| 論文年度 |
2022 |
| 論文類別 |
Article |
| 摘要 |
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. |
| 卷 |
9 |
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