ArticleBMC infectious diseases2025
A diagnostic model for sepsis using an integrated machine learning framework approach and its therapeutic drug discovery.
Article in BMC infectious diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Predictive performance of blood urea nitrogen, cystatin C, and their ratio for in-hospital mortality in sepsis: a Chinese two-center analysis.Open medicine (Warsaw, Poland) · 2026Article
- Integrated bioinformatics and mendelian randomization reveal a six-gene diagnostic signature and key role of CYP26B1 in sarcopenia.Frontiers in molecular biosciences · 2026Article
- FPR1-dependent Pro-inflammatory Ccl4Respiratory research · 2025Article
- Machine learning-based identification of leptin-associated biomarkers and prognostic prediction models in sepsis.Frontiers in cellular and infection microbiology · 2025Article
Corrections and comments
- Retracted
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSepsis remains a life-threatening condition in intensive care units (ICU) with high morbidity and mortality rates. Some biomarkers commonly used in clinic do not have the characteristics of rapid and specific growth and rapid decline after effective treatment. Machine learning has shown great potential in early diagnosis, subtype analysis, accurate treatment and prognosis evaluation of sepsis.
methodsGene expression matrices from GSE13904 and GSE26440 were combined into a training model after quality control and standardization. Then, the intersection genes were obtained by crossing the screened differentially expressed genes (DEGs) and the module genes with the strongest correlation obtained by WGCNA analysis. 113 combined machine learning algorithms to build a diagnosis model. Then the CIBERSORT algorithm is used to analyze the relationship between the change of core gene expression and immune response in sepsis. Construct nomogram, DCA and CIC to further verify the reliability of the diagnosis model. The potential molecular compounds interacting with key genes were searched from the Traditional Chinese Medicine Active Compound Library (TCMACL).
resultsWe screened 405 DEGs, including 334 up-regulated and 71 down-regulated genes. The 308 potential genes were obtained by intersection of MEturquoise module genes in WGCNA analysis and DEGs for subsequent machine learning analysis. GO and KEGG enrichment analysis showed that sepsis was mainly related to immune response and bacterial infection. Then 113 combined machine learning algorithms are applied to construct a diagnosis model to screen 22 hub genes. Four four key genes (CD177, GNLY, ANKRD22, and IFIT1) are obtained through further analysis of PPI network constructed by 22 hub genes. Subsequently, the diagnostic model is proved to have good predictive value by nomogram, DCA and CIC. Finally, molecular compounds (Dieckol, Grosvenorine and Tellimagrandin II) were screened out as potential drugs.
conclusion113 combinated machine learning algorithms screened out four key genes that can distinguish sepsis patients. At the same time, potential therapeutic molecular compounds interacting with key genes genes were screened out by molecular docking.
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