A predictive model for evaluating the risk of latent tuberculosis relapse via machine learning.

ABSTRACT:

Background

Reactivation of latent tuberculosis infection (LTBI) is a major obstacle to tuberculosis eradication. Predicting LTBI relapse is crucial for effective disease management but remains underexplored.

Methods

We analysed gene expression data from the GSE54992 dataset via differential expression analysis and weighted gene co-expression network analysis (WGCNA) to identify genes associated with LTBI relapse. A risk-predictive model was developed using three genes (ALG2, FARS2, and PGP) selected through a support vector machine with recursive feature elimination (SVM-RFE), least absolute shrinkage and selection operator (LASSO), and random forest algorithms. Model performance was assessed via the area under the receiver operating characteristic curve (AUC). Gene set enri

SUBMITTER: Wang C 

PROVIDER: S-EPMC12581492 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature