{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,6]],"date-time":"2026-11-06T04:13:15Z","timestamp":1793938395332,"version":"4.8.0"},"reference-count":50,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,8,19]],"date-time":"2024-08-19T00:00:00Z","timestamp":1724025600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:p>Lupus nephritis (LN) is a challenging condition with limited diagnostic and treatment options. In this study, we applied 12 distinct machine learning algorithms along with Non-negative Matrix Factorization (NMF) to analyze single-cell datasets from kidney biopsies, aiming to provide a comprehensive profile of LN. Through this analysis, we identified various immune cell populations and their roles in LN progression and constructed 102 machine learning-based immune-related gene (IRG) predictive models. The most effective models demonstrated high predictive accuracy, evidenced by Area Under the Curve (AUC) values, and were further validated in external cohorts. These models highlight six hub IRGs (<jats:italic>CD14<\/jats:italic>, <jats:italic>CYBB<\/jats:italic>, <jats:italic>IFNGR1<\/jats:italic>, <jats:italic>IL1B<\/jats:italic>, <jats:italic>MSR1<\/jats:italic>, and <jats:italic>PLAUR<\/jats:italic>) as key diagnostic markers for LN, showing remarkable diagnostic performance in both renal and peripheral blood cohorts, thus offering a novel approach for noninvasive LN diagnosis. Further clinical correlation analysis revealed that expressions of <jats:italic>IFNGR1, PLAUR<\/jats:italic>, and <jats:italic>CYBB<\/jats:italic> were negatively correlated with the glomerular filtration rate (GFR), while <jats:italic>CYBB<\/jats:italic> also positively correlated with proteinuria and serum creatinine levels, highlighting their roles in LN pathophysiology. Additionally, protein-protein interaction (PPI) analysis revealed significant networks involving hub IRGs, emphasizing the importance of the interleukin family and chemokines in LN pathogenesis. This study highlights the potential of integrating advanced genomic tools and machine learning algorithms to improve diagnosis and personalize management of complex autoimmune diseases like LN.<\/jats:p>","DOI":"10.3389\/fimmu.2024.1391218","type":"journal-article","created":{"date-parts":[[2024,8,19]],"date-time":"2024-08-19T04:27:05Z","timestamp":1724041625000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Applying 12 machine learning algorithms and Non-negative Matrix Factorization for robust prediction of lupus 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