Brittain Evan L, Chan Stephen Y
Division of Cardiovascular Medicine and Vanderbilt Translational and Clinical Cardiovascular Center, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Division of Cardiology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA; and Center for Pulmonary Vascular Biology and Medicine, Pittsburgh Heart, Lung, and Blood Vascular Medicine Institute, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.
Pulm Circ. 2016 Sep;6(3):251-60. doi: 10.1086/686995.
The application of complex data sources to pulmonary vascular diseases is an emerging and promising area of investigation. The use of -omics platforms, in silico modeling of gene networks, and linkage of large human cohorts with DNA biobanks are beginning to bear biologic insight into pulmonary hypertension. These approaches to high-throughput molecular phenotyping offer the possibility of discovering new therapeutic targets and identifying variability in response to therapy that can be leveraged to improve clinical care. Optimizing the methods for analyzing complex data sources and accruing large, well-phenotyped human cohorts linked to biologic data remain significant challenges. Here, we discuss two specific types of complex data sources-gene regulatory networks and DNA-linked electronic medical record cohorts-that illustrate the promise, challenges, and current limitations of these approaches to understanding and managing pulmonary vascular disease.
复杂数据源在肺血管疾病中的应用是一个新兴且有前景的研究领域。“组学”平台的使用、基因网络的计算机模拟以及大型人类队列与DNA生物样本库的关联,正开始为肺动脉高压带来生物学见解。这些高通量分子表型分析方法提供了发现新治疗靶点以及识别治疗反应变异性的可能性,而这种变异性可用于改善临床护理。优化分析复杂数据源的方法以及积累与生物数据相关的大型、表型良好的人类队列仍然是重大挑战。在此,我们讨论两种特定类型的复杂数据源——基因调控网络和与DNA相关的电子病历队列——它们说明了这些理解和管理肺血管疾病方法的前景、挑战及当前局限性。