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：
10.1155/2010/708932
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发表时间：
2010-01-01
期刊：
影响因子：
--
通讯作者：
Tilton, Susan C.
中科院分区：
文献类型：
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作者：
McDermott, Jason E.;Costa, Michelle;Tilton, Susan C.
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一场革命。越来越清楚的是，许多最重要的人类疾病是多种因素之间复杂相互作用的结果，包括环境因素，例如接触毒素或病原体、饮食、生活方式和个体患者的遗传。最近的研究已经开始在网络的背景下描述这些因素，这些网络描述了基因、蛋白质和代谢物等生物成分之间的关系，并且在将疾病理解为整个系统的功能障碍而不是单个基因的突变方面取得了进展。我们总结了该领域最近的一些工作，重点关注不同类型的补充数据的整合以及生物网络和通路的分析如何能够发现稳健、特异和有用的疾病生物标志物，以及这些方法如何帮助阐明正在研究的疾病的机制和病因学。
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.