ArticleBMC medical informatics and decision making2025
Identifying effective immune biomarkers in alopecia areata diagnosis based on machine learning methods.
Article in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The trial behind it
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Who cites it
5 citing papers in PubMed.
- Article
- Precision therapeutics in non-scarring alopecia: a systemic genomic and pathway-based framework for targeted interventions.ADMET & DMPK · 2026Review
- Early prediction of alopecia areata using machine learning modeling of neuro stress immune signatures from multi datasets.Scientific reports · 2025Article
- The Future of Alopecia Treatment: Plant Extracts, Nanocarriers, and 3D Bioprinting in Focus.Pharmaceutics · 2025Review
- Longitudinal clinical course, treatment outcomes, and relapse patterns in alopecia areata: a prospective cohort study.Frontiers in medicine · 2025Article
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Authors and funding
4 authors.
Funding
Abstract
backgroundAlopecia areata (AA) is a common non-scarring hair loss disorder associated with autoimmune conditions. However, the pathobiology of AA is not well understood, and there is no targeted therapy available for AA.
methodsIn this study, differential gene expression analysis, immune status assessment, weighted correlation network analysis (WGCNA), and functional enrichment analysis were performed to identify shared genes associated with both immunological response and AA. Machine learning methods were then used to identify three hub genes as potential diagnostic markers for AA. External validation was performed, and the correlation of hub genes with immune infiltration, immune checkpoint genes, and key marker genes and pathways were evaluated.
resultsThree hub genes were identified, which accurately predicted the progression of AA and the immune status. The hub genes were found to be diagnostic markers for AA with high predictive accuracy. External validation confirmed the efficacy of these markers in identifying AA patients.
conclusionOverall, the study provides a novel approach for the diagnosis, prevention, and treatment of AA. The findings could potentially lead to the development of targeted therapies for AA based on the identified hub genes. The study also highlights the potential of machine learning and bioinformatics analysis in identifying new biomarkers for autoimmune diseases.
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