{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["13"],"submitter":["Liu M"],"pubmed_abstract":["<h4>Background</h4>Sepsis is a life-threatening disease causing millions of deaths every year. It has been reported that programmed cell death (PCD) plays a critical role in the development and progression of sepsis, which has the potential to be a diagnosis and prognosis indicator for patient with sepsis.<h4>Methods</h4>Fourteen PCD patterns were analyzed for model construction. Seven transcriptome datasets and a single cell sequencing dataset were collected from the Gene Expression Omnibus database.<h4>Results</h4>A total of 289 PCD-related differentially expressed genes were identified between sepsis patients and healthy individuals. The machine learning algorithm screened three PCD-related genes, NLRC4, TXN and S100A9, as potential biomarkers for sepsis. The area under curve of the dia"],"journal":["PeerJ"],"pagination":["e19077"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11871900"],"repository":["biostudies-literature"],"pubmed_title":["Leveraging diverse cell-death patterns in diagnosis of sepsis by integrating bioinformatics and machine learning."],"pmcid":["PMC11871900"],"pubmed_authors":["Li X","Sheng Y","Liu M","Zhang Y","Gao X","Wang H","Zhu R"],"additional_accession":[]},"is_claimable":false,"name":"Leveraging diverse cell-death patterns in diagnosis of sepsis by integrating bioinformatics and machine learning.","description":"<h4>Background</h4>Sepsis is a life-threatening disease causing millions of deaths every year. It has been reported that programmed cell death (PCD) plays a critical role in the development and progression of sepsis, which has the potential to be a diagnosis and prognosis indicator for patient with sepsis.<h4>Methods</h4>Fourteen PCD patterns were analyzed for model construction. Seven transcriptome datasets and a single cell sequencing dataset were collected from the Gene Expression Omnibus database.<h4>Results</h4>A total of 289 PCD-related differentially expressed genes were identified between sepsis patients and healthy individuals. The machine learning algorithm screened three PCD-related genes, NLRC4, TXN and S100A9, as potential biomarkers for sepsis. The area under curve of the dia","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025","modification":"2026-06-02T19:15:27.099Z","creation":"2025-04-04T02:47:46.621Z"},"accession":"S-EPMC11871900","cross_references":{"pubmed":["40028203"],"doi":["10.7717/peerj.19077"]}}