{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Chen L"],"funding":["Chinese Academy of Sciences"],"pagination":["6089242"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9073549"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["2022"],"pubmed_abstract":["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<sup>+</sup> cell, CD8<sup>+</sup> 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 mRM"],"journal":["BioMed research international"],"pubmed_title":["Recognition of Immune Cell Markers of COVID-19 Severity with Machine Learning Methods."],"pmcid":["PMC9073549"],"funding_grant_id":["XDA26040304","XDB38050200"],"pubmed_authors":["Huang T","Ding S","Chen L","Mei Z","Guo W","Cai YD"],"additional_accession":[]},"is_claimable":false,"name":"Recognition of Immune Cell Markers of COVID-19 Severity with Machine Learning Methods.","description":"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<sup>+</sup> cell, CD8<sup>+</sup> 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 mRM","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022","modification":"2026-05-31T01:01:36.342Z","creation":"2025-04-04T23:34:13.373Z"},"accession":"S-EPMC9073549","cross_references":{"pubmed":["35528178"],"doi":["10.1155/2022/6089242"]}}