{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["13"],"submitter":["Wang P"],"pubmed_abstract":["<h4>Introduction</h4>Viral infection, typically disregarded, has a significant role in burns. However, there is still a lack of biomarkers and immunotherapy targets related to viral infections in burns.<h4>Methods</h4>Virus-related genes (VRGs) that were extracted from Gene Oncology (GO) database were included as hallmarks. Through unsupervised consensus clustering, we divided patients into two VRGs molecular patterns (VRGMPs). Weighted gene co-expression network analysis (WGCNA) was performed to study the relationship between burns and VRGs. Random forest (RF), least absolute shrinkage and selection operator (LASSO) regression, and logistic regression were used to select key genes, which were utilized to construct prognostic signatures by multivariate logistic regression. The risk score o"],"journal":["Frontiers in immunology"],"pagination":["1054407"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9742460"],"repository":["biostudies-literature"],"pubmed_title":["Machine learning links different gene patterns of viral infection to immunosuppression and immune-related biomarkers in severe burns."],"pmcid":["PMC9742460"],"pubmed_authors":["Lai H","Liu J","Wang P","Lin R","Jiang L","Zhang Z","Xiao H","Lin J","Wang Y","Zhou X","Deng X"],"additional_accession":[]},"is_claimable":false,"name":"Machine learning links different gene patterns of viral infection to immunosuppression and immune-related biomarkers in severe burns.","description":"<h4>Introduction</h4>Viral infection, typically disregarded, has a significant role in burns. However, there is still a lack of biomarkers and immunotherapy targets related to viral infections in burns.<h4>Methods</h4>Virus-related genes (VRGs) that were extracted from Gene Oncology (GO) database were included as hallmarks. Through unsupervised consensus clustering, we divided patients into two VRGs molecular patterns (VRGMPs). Weighted gene co-expression network analysis (WGCNA) was performed to study the relationship between burns and VRGs. Random forest (RF), least absolute shrinkage and selection operator (LASSO) regression, and logistic regression were used to select key genes, which were utilized to construct prognostic signatures by multivariate logistic regression. The risk score o","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022","modification":"2025-06-01T01:56:48.486Z","creation":"2025-06-01T01:56:48.486Z"},"accession":"S-EPMC9742460","cross_references":{"pubmed":["36518755"],"doi":["10.3389/fimmu.2022.1054407"]}}