Machine learning and bioinformatics framework integration reveal potential characteristic genes related to immune cell infiltration in preeclampsia.

ABSTRACT: Introduction: Preeclampsia is a disease that affects both the mother and child, with serious consequences. Screening the characteristic genes of preeclampsia and studying the placental immune microenvironment are expected to explore specific methods for the treatment of preeclampsia and gain an in-depth understanding of the pathological mechanism of preeclampsia. Methods: We screened for differential genes in preeclampsia by using limma package. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, disease ontology enrichment, and gene set enrichment analyses were performed. Analysis and identification of preeclampsia biomarkers were performed by using the least absolute shrinkage and selection operator regression model, support vector machine recursive feature elimination,

SUBMITTER: Bai L 

PROVIDER: S-EPMC10300062 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature