Machine learning-based identification and validation of amino acid metabolism related genes as novel biomarkers in chronic kidney disease.

ABSTRACT:

Objectives

Chronic kidney disease (CKD) is a progressive illness with a high rate of morbidity and mortality with no proven therapy. Alterations of amino acid(AA) metabolism are associated with the incidence and progression of CKD. To characterize the potential value of AA metabolism related genes in the diagnosis and progression of CKD.

Methods

We filtered the key genes associated with AA metabolism based on the least absolute shrinkage and selection operator (LASSO) and SVM algorithm. Then, we constructed logistic regression models and evaluated the accuracy and specificity by nomogram analysis and DCA. Also, we mapped the ROC curves.Meanwhile, in order to determine the underlying mechanism and relevant biological features of CKD, we conducted differential analysis between

SUBMITTER: Zhang G 

PROVIDER: S-EPMC11786826 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature