ArticleClinical and experimental medicine2025
Identification of potential biomarkers for Lyme disease using bioinformatics and machine learning.
Article in Clinical and experimental medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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Who cites it
1 citing paper in PubMed.
- Epidemiology, diagnosis and emerging therapies for Lyme disease of the Northern Hemisphere.International journal of emergency medicine · 2026Review
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Authors and funding
9 authors.
Funding
Abstract
Lyme disease (LD) presents significant diagnostic challenges due to the absence of a reliable screening method for initial detection. This study aimed to identify potential biomarkers using bioinformatics and machine learning algorithms, which may contribute to future biomarker-based research for Lyme disease diagnostics. The gene expression profile datasets GSE145974 and GSE63085 were analyzed using machine learning to identify hub genes among differentially expressed genes. High-throughput data and receiver operating characteristic curves were used to validate these genes. The molecular mechanisms underlying LD were explored using functional enrichment analysis. The correlation between immune cell counts and insomnia in LD was further validated using clinical data from the GEO database. Gene set enrichment analysis indicated that hub genes were enriched in circadian rhythms. The integration of machine learning revealed FCGR1B, MPP1, and HSPA6 as potential central genes involved in immune response and diagnostic biomarkers for Lyme disease. Immune infiltration analysis showed that LD is frequently associated with the monocyte-macrophage system and humoral immunity. This study provides novel insights into the targeted treatment of LD by revealing novel diagnostic biomarkers.
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