肺部疾病是全球超过500万人死亡的原因，并对医疗保健造成负担。改善临床结果，包括死亡率和生活质量，需要对疾病有一个整体性的理解，而这可以通过整合肺部多组学数据来实现。全面多组学数据集的深入理解为利用这些数据集来指导肺部疾病的治疗和预防，包括严重程度分类、预后判断和生物标志物的发现提供了机会。本综述的主要目的是总结多组学研究在肺部疾病中的应用，包括多组学整合和机器学习计算方法的应用。本综述还讨论了研究肺部健康和疾病中多组学的肺部疾病模型，包括动物模型、器官样品和单细胞系。我们提供了多组学研究在肺部疾病中提供更深入病因病理机制认识，并导致改善预防和治疗干预的示例。

Diseases of the lung account for more than 5 million deaths worldwide and are a healthcare burden. Improving clinical outcomes, including mortality and quality of life, involves a holistic understanding of the disease, which can be provided by the integration of lung multi-omics data. An enhanced understanding of comprehensive multiomic datasets provides opportunities to leverage those datasets to inform the treatment and prevention of lung diseases by classifying severity, prognostication, and discovery of biomarkers. The main objective of this review is to summarize the use of multiomics investigations in lung disease, including multiomics integration and the use of machine learning computational methods. This review also discusses lung disease models, including animal models, organoids, and single-cell lines, to study multiomics in lung health and disease. We provide examples of lung diseases where multi-omics investigations have provided deeper insight into etiopathogenesis and have resulted in improved preventative and therapeutic interventions.