Exploring IRGs as a Biomarker of Pulmonary Hypertension Using Multiple Machine Learning Algorithms.

ABSTRACT:

Background

Pulmonary arterial hypertension (PAH) is a severe disease with poor prognosis and high mortality, lacking simple and sensitive diagnostic biomarkers in clinical practice. This study aims to identify novel diagnostic biomarkers for PAH using genomics research.

Methods

We conducted a comprehensive analysis of a large transcriptome dataset, including PAH and inflammatory response genes (IRGs), integrated with 113 machine learning models to assess diagnostic potential. We developed a clinical diagnostic model based on hub genes, evaluating their effectiveness through calibration curves, clinical decision curves, and ROC curves. An animal model of PAH was also established to validate hub gene expression patterns.

Results

Among the 113 machine learning algorithms

SUBMITTER: Yang J 

PROVIDER: S-EPMC11545203 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature