肺动脉高压
精密医学
杠杆(统计)
疾病
生物信息学
个性化医疗
仿形(计算机编程)
医学
计算生物学
模式
药物发现
大数据
计算机科学
数据科学
生物
数据挖掘
机器学习
病理
内科学
社会学
操作系统
社会科学
作者
Christopher J. Rhodes,Andrew J. Sweatt,Bradley A. Maron
出处
期刊:Circulation Research
[Ovid Technologies (Wolters Kluwer)]
日期:2022-04-29
卷期号:130 (9): 1423-1444
被引量:22
标识
DOI:10.1161/circresaha.121.319969
摘要
Pulmonary hypertension is a complex disease with multiple causes, corresponding to phenotypic heterogeneity and variable therapeutic responses. Advancing understanding of pulmonary hypertension pathogenesis is likely to hinge on integrated methods that leverage data from health records, imaging, novel molecular -omics profiling, and other modalities. In this review, we summarize key data sets generated thus far in the field and describe analytical methods that hold promise for deciphering the molecular mechanisms that underpin pulmonary vascular remodeling, including machine learning, network medicine, and functional genetics. We also detail how genetic and subphenotyping approaches enable earlier diagnosis, refined prognostication, and optimized treatment prediction. We propose strategies that identify functionally important molecular pathways, bolstered by findings across multi-omics platforms, which are well-positioned to individualize drug therapy selection and advance precision medicine in this highly morbid disease.
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