Multiomic Investigations into Lung Health and Disease.

Multiomic Investigations into Lung Health and Disease.
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肺部健康和疾病的多组学研究。

DOI:
10.3390/microorganisms11082116
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发表时间:
2023-08-19
期刊:
影响因子:
4.5
通讯作者:
--
中科院分区:
生物学3区
文献类型:
--
作者:

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肺部疾病在全球造成500多万人死亡,是医疗保健的负担。改善临床结果,包括死亡率和生活质量,涉及对疾病的全面了解,这可以通过整合肺部多组学数据来提供。对综合性多组学数据集的深入理解提供了利用这些数据集通过对严重程度、预测和生物标记物的发现进行分类来为肺部疾病的治疗和预防提供信息的机会。该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使用。该综述还讨论了肺部疾病模型,包括动物模型、有机化合物和单细胞系,以研究多组学在肺部健康和疾病中的作用。我们提供了肺部疾病的例子,其中多组学研究提供了对病因的更深层次的了解,并导致了改进的预防和治疗干预措施。
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.
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