ArticleBiomedicines2023
Bioinformatic Analysis and Machine Learning Methods in Neonatal Sepsis: Identification of Biomarkers and Immune Infiltration.
Article in Biomedicines, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Comparison of the diagnostic accuracy of resistin and CRP levels for sepsis in neonates and children: a systematic review and meta-analysis.Frontiers in pediatrics · 2025Pooled it
- RETN exacerbates sepsis by GBP5/NLRP3 signaling pathway-mediated pyroptosis of macrophage.Genes and immunity · 2026Article
- Identification and validation of novel candidate genes with diagnostic value for sepsis via weighted gene co-expression network analysis.Frontiers in medicine · 2026Article
- Diagnostic performance of interleukin-27 and C-reactive protein in neonatal sepsis: an updated systematic review and meta-analysis.Frontiers in pediatrics · 2026Review
- Molecular Mechanisms Underlying Inflammation in Early-Onset Neonatal Sepsis: A Systematic Review of Human Studies.Journal of clinical medicine · 2025Review
- Integrative Analyses of Bulk and Single-Cell RNA Seq Identified the Shared Genes in Acute Respiratory Distress Syndrome and Rheumatoid Arthritis.Molecular biotechnology · 2025Article
- Identification of amino acid metabolism‑related genes as diagnostic and prognostic biomarkers in sepsis through machine learning.Experimental and therapeutic medicine · 2025Article
- Identification of Immune-Related Genes as Potential Biomarkers in Early Septic Shock.International archives of allergy and immunology · 2025Article
- Identification of secretory protein related biomarkers for primary biliary cholangitis based on machine learning and experimental validation.Open life sciences · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The disease neonatal sepsis (NS) poses a serious threat to life, and its pathogenesis remains unclear. Using the Gene Expression Omnibus (GEO) database, differentially expressed genes (DEGs) were identified and functional enrichment analyses were conducted. Three machine learning algorithms containing the least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF) were applied to identify the optimal feature genes (OFGs). This study conducted CIBERSORT to present the abundance of immune infiltrates between septic and control neonates and assessed the relationship between OFGs and immune cells. In total, 44 DEGs were discovered between the septic and control newborns. Throughout the enrichment analysis, DEGs were primarily related to inflammatory signaling pathways and immune responses. The OFGs derived from machine learning algorithms were intersected to yield four biomarkers, namely Hexokinase 3 (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.