biostudies-literatureChen LChinese Academy of Sciences6089242https://www.ebi.ac.uk/biostudies/studies/S-EPMC9073549biostudies-literatureUnknown2022COVID-19 is hypothesized to be linked to the host's excessive inflammatory immunological response to SARS-CoV-2 infection, which is regarded to be a major factor in disease severity and mortality. Numerous immune cells play a key role in immune response regulation, and gene expression analysis in these cells could be a useful method for studying disease states, assessing immunological responses, and detecting biomarkers. Here, we developed a machine learning procedure to find biomarkers that discriminate disease severity in individual immune cells (B cell, CD4⁺ cell, CD8⁺ cell, monocyte, and NK cell) using single-cell gene expression profiles of COVID-19. The gene features of each profile were first filtered and ranked using the Boruta feature selection method and mRMBioMed research internationalRecognition of Immune Cell Markers of COVID-19 Severity with Machine Learning Methods.PMC9073549XDA26040304XDB38050200Huang TDing SChen LMei ZGuo WCai YDfalseRecognition of Immune Cell Markers of COVID-19 Severity with Machine Learning Methods.COVID-19 is hypothesized to be linked to the host's excessive inflammatory immunological response to SARS-CoV-2 infection, which is regarded to be a major factor in disease severity and mortality. Numerous immune cells play a key role in immune response regulation, and gene expression analysis in these cells could be a useful method for studying disease states, assessing immunological responses, and detecting biomarkers. Here, we developed a machine learning procedure to find biomarkers that discriminate disease severity in individual immune cells (B cell, CD4⁺ cell, CD8⁺ cell, monocyte, and NK cell) using single-cell gene expression profiles of COVID-19. The gene features of each profile were first filtered and ranked using the Boruta feature selection method and mRM2022-01-01T00:00:00Z20222026-05-31T01:01:36.342Z2025-04-04T23:34:13.373ZS-EPMC90735493552817810.1155/2022/6089242