Pulmonary arterial hypertension (PAH) is a severe disease characterized by abnormal pulmonary vascular remodeling and increased right ventricular pressure load, posing a significant threat to patient health. While some pathological mechanisms of PAH have been revealed, the deeper mechanisms of pathogenesis remain to be elucidated. In recent years, bioinformatics has provided a powerful tool for a deeper understanding of the complex mechanisms of PAH through the integration of techniques such as multi-omics analysis, artificial intelligence, and Mendelian randomization. This review focuses on the bioinformatics methods and technologies used in PAH research, summarizing their current applications in the study of disease mechanisms, diagnosis, and prognosis assessment. Additionally, it analyzes the existing challenges faced by bioinformatics and its potential applications in the clinical and basic research fields of PAH in the future.
肺动脉高压（pulmonary arterial hypertension, PAH）是一种以肺血管异常重塑和右心室压力负荷增加为特征的严重疾病，对患者健康构成重大威胁。尽管PAH的部分病理机制已被揭示，但更深入的致病机制尚待阐明。近年来，生物信息学通过多组学分析、人工智能和孟德尔随机化等技术的融合，为深入理解PAH的复杂机制提供了强有力的工具。该综述着重探讨在PAH研究中所采用的生物信息学方法和技术，总结其在疾病机制研究、诊断和预后评估方面的应用现状。此外，该文深入分析了生物信息学面临的现有挑战，以及未来其在PAH临床和基础研究领域的潜在应用。.
Keywords: Artificial intelligence; Bioinformatics; Machine learning; Pulmonary arterial hypertension.