ArticleFrontiers in immunology2024
Applying 12 machine learning algorithms and Non-negative Matrix Factorization for robust prediction of lupus nephritis.
Article in Frontiers in immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Identification of ATF3 as a Potential Key Biomarker for Hypertensive Nephropathy via Single-Cell Transcriptome Sequencing.Inflammation · 2026Article
- The chemokine network in lupus nephritis: pathogenesis, targeted therapies, and future directions.Frontiers in pharmacology · 2026Review
- Machine learning in lupus nephritis: bridging prediction models and clinical decision-making towards personalized nephrology.Frontiers in medicine · 2025Review
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6 authors.
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Abstract
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 (
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