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

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 (CD14, CYBB, IFNGR1, IL1B, MSR1, and PLAUR) as key diag

SUBMITTER: Mou L 

PROVIDER: S-EPMC11366613 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature