ArticleFrontiers in immunology2026
Identification of psoriasis-associated hub genes by integrated transcriptomics and machine learning.
Article in Frontiers in immunology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
This study aims to systematically identify key hub genes in psoriasis pathogenesis through the integration of bioinformatics, machine learning and experimental validation methods. Four psoriasis microarray datasets were retrieved from the GEO database, with differentially expressed genes identified through comparative analysis. A dual machine learning approach screened core hub genes, whose expression levels and diagnostic efficacy were validated in an independent cohort. CIBERSORT was employed for immune cell infiltration analysis, whilst a multi-database approach constructed an mRNA-miRNA-lncRNA regulatory network. Molecular docking technology assessed interactions between the environmental pollutant bisphenol A, predicted drugs, and hub genes. Finally, an
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