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

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...

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Vydané v:Frontiers in immunology Ročník 15; s. 1391218
Hlavní autori: Mou, Lisha, Lu, Ying, Wu, Zijing, Pu, Zuhui, Huang, Xiaoyan, Wang, Meiying
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Switzerland Frontiers Media SA 19.08.2024
Frontiers Media S.A
Predmet:
Adult
Algorithms
Autoimmune diseases
Biomarkers
Biopsy
CD14 antigen
Chemokines
Computational Biology - methods
Correlation analysis
Creatinine
Data analysis
Datasets
Diagnosis
Disease
Female
Gene expression
Gene Expression Profiling
Generalized linear models
Glomerular filtration rate
Humans
immune-related genes
Immunology
Learning algorithms
Leukocytes
Lupus
Lupus nephritis
Lupus Nephritis - diagnosis
Lupus Nephritis - immunology
Machine Learning
Male
NMF
Peripheral blood
Prediction models
Protein interaction
Protein Interaction Maps
Proteins
Proteinuria
scRNA-seq
Single-Cell Analysis - methods
Systemic lupus erythematosus
Japan
scRNA-seq
systemic lupus erythematosus
prediction model
NMF
PPI
lupus nephritis
immune-related genes
machine learning
ISSN:1664-3224, 1664-3224
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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Simone Parisi, University Hospital of the City of Health and Science of Turin, Italy
Edited by: Carlo Perricone, University of Perugia, Italy
Reviewed by: Roberto Dal Pozzolo, University of Perugia, Italy
ISSN:1664-3224
1664-3224
DOI:10.3389/fimmu.2024.1391218