Machine learning potential predictor of idiopathic pulmonary fibrosis.

ABSTRACT:

Introduction

Idiopathic pulmonary fibrosis (IPF) is a severe chronic respiratory disease characterized by treatment challenges and poor prognosis. Identifying relevant biomarkers for effective early-stage risk prediction is therefore of critical importance.

Methods

In this study, we obtained gene expression profiles and corresponding clinical data of IPF patients from the GEO database. GO enrichment and KEGG pathway analyses were performed using R software. To construct an IPF risk prediction model, we employed LASSO-Cox regression analysis and the SVM-RFE algorithm. PODNL1 and PIGA were identified as potential biomarkers associated with IPF onset, and their predictive accuracy was confirmed using ROC curve analysis in the test set. Furthermore, GSEA revealed enrichment in m

SUBMITTER: Ding C 

PROVIDER: S-EPMC11811625 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature