Separating the drivers from the driven: Integrative network and pathway approaches aid identification of disease biomarkers from high-throughput data.
Separating the drivers from the driven: Integrative network and pathway approaches aid identification of disease biomarkers from high-throughput data.
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DOI：
10.3233/dma-2010-0695
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发表时间：
2010
期刊：
影响因子：
--
通讯作者：
Tilton SC
中科院分区：
文献类型：
--
作者：
McDermott JE;Costa M;Janszen D;Singhal M;Tilton SC
高通量数据采集的最新进展推动了人类疾病研究和疾病状态分子生物标志物测定的革命。越来越清楚的是，许多最重要的人类疾病是由于若干因素之间复杂的相互作用而产生的，这些因素包括环境因素，如暴露于毒素或病原体、饮食、生活方式和个体患者的遗传学。最近的研究已经开始在描述生物成分如基因、蛋白质和代谢物之间关系的网络背景下描述这些因素，并且在将疾病理解为整个系统的功能障碍而不是例如单个基因的突变方面取得了进展。我们总结了这一领域最近的一些工作，重点是如何整合不同类型的互补数据，分析生物网络和途径，从而发现强大的，特异性的和有用的疾病生物标志物，以及这些方法如何有助于阐明正在研究的疾病的机制和病因。
The recent advances in high-throughput data acquisition have driven a revolution in the study of human disease and determination of molecular biomarkers of disease states. It has become increasingly clear that many of the most important human diseases arise as the result of a complex interplay between several factors including environmental factors, such as exposure to toxins or pathogens, diet, lifestyle, and the genetics of the individual patient. Recent research has begun to describe these factors in the context of networks which describe relationships between biological components, such as genes, proteins and metabolites, and have made progress towards the understanding of disease as a dysfunction of the entire system, rather than, for example, mutations in single genes. We provide a summary of some of the recent work in this area, focusing on how the integration of different kinds of complementary data, and analysis of biological networks and pathways can lead to discovery of robust, specific and useful biomarkers of disease and how these methods can help shed light on the mechanisms and etiology of the diseases being studied.