Exploring hypoxia driven subtypes of pulmonary arterial hypertension through transcriptomics single cell sequencing and machine learning.

ABSTRACT: Pulmonary arterial hypertension (PAH) is a progressive cardiovascular disease characterized by elevated pulmonary arterial pressure, leading to right heart failure and death. Despite advancements in diagnosis and treatment, it remains incurable, and its mechanisms are poorly understood. This study aimed to integrate multi-omics data analysis and machine learning techniques to uncover the molecular characteristics and subtypes of PAH, providing insights into precise diagnosis and therapeutic strategies. We employed consensus clustering to classify PAH patients into subgroups based on multi-omics data. Differential expression and enrichment analyses were conducted to identify key genes and pathways. Machine learning models were developed to predict PAH subtypes and assess their diagnostic pe

SUBMITTER: Xiong J 

PROVIDER: S-EPMC12022167 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature