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Applying 12 machine learning algorithms and Non-negative Matrix Factorization for robust prediction of lupus nephritis

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Title: Applying 12 machine learning algorithms and Non-negative Matrix Factorization for robust prediction of lupus nephritis
Authors: Lisha Mou, Ying Lu, Zijing Wu, Zuhui Pu, Xiaoyan Huang, Meiying Wang
Source: Frontiers in Immunology, Vol 15 (2024)
Publisher Information: Frontiers Media S.A., 2024.
Publication Year: 2024
Collection: LCC:Immunologic diseases. Allergy
Subject Terms: systemic lupus erythematosus, lupus nephritis, scRNA-seq, immune-related genes, NMF, machine learning, Immunologic diseases. Allergy, RC581-607
More Details: 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 (CD14, CYBB, IFNGR1, IL1B, MSR1, and PLAUR) 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 IFNGR1, PLAUR, and CYBB were negatively correlated with the glomerular filtration rate (GFR), while CYBB 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.
Document Type: article
File Description: electronic resource
Language: English
ISSN: 1664-3224
Relation: https://www.frontiersin.org/articles/10.3389/fimmu.2024.1391218/full; https://doaj.org/toc/1664-3224
DOI: 10.3389/fimmu.2024.1391218
Access URL: https://doaj.org/article/90f7497bcdd14c6cb376774980af93ee
Accession Number: edsdoj.90f7497bcdd14c6cb376774980af93ee
Database: Directory of Open Access Journals
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  Data: Applying 12 machine learning algorithms and Non-negative Matrix Factorization for robust prediction of lupus nephritis
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  Data: <searchLink fieldCode="AR" term="%22Lisha+Mou%22">Lisha Mou</searchLink><br /><searchLink fieldCode="AR" term="%22Ying+Lu%22">Ying Lu</searchLink><br /><searchLink fieldCode="AR" term="%22Zijing+Wu%22">Zijing Wu</searchLink><br /><searchLink fieldCode="AR" term="%22Zuhui+Pu%22">Zuhui Pu</searchLink><br /><searchLink fieldCode="AR" term="%22Xiaoyan+Huang%22">Xiaoyan Huang</searchLink><br /><searchLink fieldCode="AR" term="%22Meiying+Wang%22">Meiying Wang</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22systemic+lupus+erythematosus%22">systemic lupus erythematosus</searchLink><br /><searchLink fieldCode="DE" term="%22lupus+nephritis%22">lupus nephritis</searchLink><br /><searchLink fieldCode="DE" term="%22scRNA-seq%22">scRNA-seq</searchLink><br /><searchLink fieldCode="DE" term="%22immune-related+genes%22">immune-related genes</searchLink><br /><searchLink fieldCode="DE" term="%22NMF%22">NMF</searchLink><br /><searchLink fieldCode="DE" term="%22machine+learning%22">machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Immunologic+diseases%2E+Allergy%22">Immunologic diseases. Allergy</searchLink><br /><searchLink fieldCode="DE" term="%22RC581-607%22">RC581-607</searchLink>
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  Data: 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 (CD14, CYBB, IFNGR1, IL1B, MSR1, and PLAUR) 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 IFNGR1, PLAUR, and CYBB were negatively correlated with the glomerular filtration rate (GFR), while CYBB 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.
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        Value: 10.3389/fimmu.2024.1391218
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      – Text: English
    Subjects:
      – SubjectFull: systemic lupus erythematosus
        Type: general
      – SubjectFull: lupus nephritis
        Type: general
      – SubjectFull: scRNA-seq
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      – SubjectFull: immune-related genes
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      – SubjectFull: RC581-607
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      – TitleFull: Applying 12 machine learning algorithms and Non-negative Matrix Factorization for robust prediction of lupus nephritis
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