Department of Pathology, University of Washington, Seattle, WA, USA.
Arterioscler Thromb Vasc Biol. 2012 Dec;32(12):2821-35. doi: 10.1161/ATVBAHA.112.300123. Epub 2012 Oct 18.
The combination of systems biology and large data sets offers new approaches to the study of cardiovascular diseases. These new approaches are especially important for the common cardiovascular diseases that have long been described as multifactorial. This promise is undermined by biologists' skepticism of the spider web-like network diagrams required to analyze these large data sets. Although these spider webs resemble composites of the familiar biochemical pathway diagrams, the complexity of the webs is overwhelming. As a result, biologists collaborate with data analysts whose mathematical methods seem much like those of experts using Ouija boards. To make matters worse, it is not evident how to design experiments when the network implies that many molecules must be part of the disease process. Our goal is to remove some of this mystery and suggest a simple experimental approach to the design of experiments appropriate for such analysis. We will attempt to explain how combinations of data sets that include all possible variables, graphical diagrams, complementation of different data sets, and Bayesian analyses now make it possible to determine the causes of multifactorial cardiovascular disease. We will describe this approach using the term causal analysis. Finally, we will describe how causal analysis is already being used to decipher the interactions among cytokines as causes of cardiovascular disease.
系统生物学和大数据集的结合为心血管疾病的研究提供了新的方法。这些新方法对于长期以来被描述为多因素的常见心血管疾病尤其重要。然而，生物学家对分析这些大数据集所需的蜘蛛网状网络图表示怀疑，这破坏了这一前景。尽管这些蜘蛛网类似于熟悉的生化途径图的组合，但网络的复杂性令人难以承受。因此，生物学家与数据分析人员合作，这些数据分析人员的数学方法似乎很像使用 Ouija 板的专家的方法。更糟糕的是，当网络暗示许多分子必须是疾病过程的一部分时，设计实验并不明显。我们的目标是消除其中的一些神秘感，并提出一种简单的实验方法来设计适合这种分析的实验。我们将尝试解释如何组合包含所有可能变量的数据集、图形图表、不同数据集的互补以及贝叶斯分析，现在可以确定多因素心血管疾病的原因。我们将使用“因果分析”一词来描述这种方法。最后，我们将描述因果分析如何用于破译细胞因子作为心血管疾病原因的相互作用。
