A Machine Learning Analysis of Prognostic Genes Associated With Allograft Tolerance After Renal Transplantation.

ABSTRACT: In this study, we aimed to identify transplantation tolerance (TOL)-related gene signature and use it to predict the different types of renal allograft rejection performances in kidney transplantation. Gene expression data were obtained from the Gene Expression Omnibus (GEO) database, differently expressed genes (DEGs) were performed, and the gene ontology (GO) function enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were also conducted. The machine learning methods were combined to analyze the feature TOL-related genes and verify their predictive performance. Afterward, the gene expression levels and predictive performances of TOL-related genes were conducted in the context of acute rejection (AR), chronic rejection (CR), and graft loss through he

SUBMITTER: Li Z 

PROVIDER: S-EPMC10475226 | biostudies-literature | 2023 Jan-Dec

REPOSITORIES: biostudies-literature