A dynamic network approach for the study of human phenotypes.-猫眼课题宝

DOI：

10.1371/journal.pcbi.1000353

发表时间：

2009-04

期刊：

PLoS computational biology

影响因子：

4.3

通讯作者：

中科院分区：

生物学2区

文献类型：

作者：

Hidalgo CA;Blumm N;Barabási AL;Christakis NA

来源链接：

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2661364/?report=classic

使用网络来整合不同的遗传、蛋白质组和代谢数据集已被提出作为阐明特定疾病起源的可行途径。在这里，我们介绍了一个新的表型数据库，总结了从表型疾病网络（PDN）中超过3000万患者的病史中获得的相关性。我们提出的证据表明，PDN的结构是相关的疾病进展的理解，显示：（1）患者发展的疾病接近网络中的那些他们已经有;（2）疾病的进展沿着链接的网络是不同的患者的不同性别和种族;（3）被诊断患有在PDN中更高度关联的疾病的患者倾向于比那些受较少关联疾病影响的患者更早死亡;（4）PDN中先于其他疾病的疾病往往比先于其他疾病的疾病更有关联性，并且与更高的死亡率相关。我们的研究结果表明，疾病的进展可以用网络方法来表示和研究，这有可能提高我们对人类疾病起源和演变的理解。本文介绍的数据集与本出版物同时发布，代表了研究界公开可用的最大关系表型资源。为了帮助理解生理故障，疾病被定义为影响一个或多个生理系统的特定表型集。然而，生物系统的复杂性意味着我们对疾病的工作定义是对复杂表型空间的仔细离散化。为了调和疾病的离散性与生物有机体的复杂性，我们需要了解疾病是如何联系在一起的，因为这些不同离散类别之间的联系可以提供有关导致生理故障的机制的信息。在这里，我们介绍了表型疾病网络（PDN）作为一个地图，总结疾病之间的表型连接，并显示疾病的进展优先沿着这个地图的链接。此外，我们发现，这种进展对于不同性别和种族背景的患者是不同的，并且受PDN中与许多其他疾病相关的疾病影响的患者往往比受较少相关疾病影响的患者更早死亡。此外，我们还创建了一个可查询的在线数据库（http：//hudine.neu.edu/），其中包含本研究中超过3100万例患者生成的18个不同数据集。疾病关联可以在线探索或批量下载。

The use of networks to integrate different genetic, proteomic, and metabolic datasets has been proposed as a viable path toward elucidating the origins of specific diseases. Here we introduce a new phenotypic database summarizing correlations obtained from the disease history of more than 30 million patients in a Phenotypic Disease Network (PDN). We present evidence that the structure of the PDN is relevant to the understanding of illness progression by showing that (1) patients develop diseases close in the network to those they already have; (2) the progression of disease along the links of the network is different for patients of different genders and ethnicities; (3) patients diagnosed with diseases which are more highly connected in the PDN tend to die sooner than those affected by less connected diseases; and (4) diseases that tend to be preceded by others in the PDN tend to be more connected than diseases that precede other illnesses, and are associated with higher degrees of mortality. Our findings show that disease progression can be represented and studied using network methods, offering the potential to enhance our understanding of the origin and evolution of human diseases. The dataset introduced here, released concurrently with this publication, represents the largest relational phenotypic resource publicly available to the research community. To help the understanding of physiological failures, diseases are defined as specific sets of phenotypes affecting one or several physiological systems. Yet, the complexity of biological systems implies that our working definitions of diseases are careful discretizations of a complex phenotypic space. To reconcile the discrete nature of diseases with the complexity of biological organisms, we need to understand how diseases are connected, as connections between these different discrete categories can be informative about the mechanisms causing physiological failures. Here we introduce the Phenotypic Disease Network (PDN) as a map summarizing phenotypic connections between diseases and show that diseases progress preferentially along the links of this map. Furthermore, we show that this progression is different for patients with different genders and racial backgrounds and that patients affected by diseases that are connected to many other diseases in the PDN tend to die sooner than those affected by less connected diseases. Additionally, we have created a queryable online database (http://hudine.neu.edu/) of the 18 different datasets generated from the more than 31 million patients in this study. The disease associations can be explored online or downloaded in bulk.