Development and validation of preeclampsia predictive models using key genes from bioinformatics and machine learning approaches.

ABSTRACT:

Background

Preeclampsia (PE) poses significant diagnostic and therapeutic challenges. This study aims to identify novel genes for potential diagnostic and therapeutic targets, illuminating the immune mechanisms involved.

Methods

Three GEO datasets were analyzed, merging two for training set, and using the third for external validation. Intersection analysis of differentially expressed genes (DEGs) and WGCNA highlighted candidate genes. These were further refined through LASSO, SVM-RFE, and RF algorithms to identify diagnostic hub genes. Diagnostic efficacy was assessed using ROC curves. A predictive nomogram and fully Connected Neural Network (FCNN) were developed for PE prediction. ssGSEA and correlation analysis were employed to investigate the immune landscape. Further va

SUBMITTER: Li Q 

PROVIDER: S-EPMC11560445 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature