Applying 12 machine learning algorithms and non-negative matrix factorization for robust prediction of lupus nephritis

HIGHLIGHTS

• What: The authors use a combination of single-cell_sequencing and machine_learning technologies to comprehensively investigate the transcriptional and immune profiles of LN, identifying key immune-related genes that could serve as potential therapeutic targets. With the help of these advancing analytical techniques, the research aims to delineate the complex interactions and molecular signatures of LN, to improve outcomes for patients through more precise diagnostics and targeted therapies. This model demonstrated superior predictive accuracy, as evidenced by high AUC values, in both training datasets (comprising GSE32591 and GSE113342) and validation datasets (GSE200306 and GSE81622), shown in Supplementary Figure . . .
  

   

  

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