Modules, networks and systems medicine for understanding disease and aiding diagnosis.
Modules, networks and systems medicine for understanding disease and aiding diagnosis.
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DOI：
10.1186/s13073-014-0082-6
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发表时间：
2014
期刊：
影响因子：
12.3
通讯作者：
Benson M
中科院分区：
文献类型：
--
作者：
Gustafsson M;Nestor CE;Zhang H;Barabási AL;Baranzini S;Brunak S;Chung KF;Federoff HJ;Gavin AC;Meehan RR;Picotti P;Pujana MÀ;Rajewsky N;Smith KG;Sterk PJ;Villoslada P;Benson M
许多常见疾病，例如哮喘，糖尿病或肥胖症，都涉及数千种基因之间的相互作用。高通量技术（OMICS）允许识别此类基因及其产品，但功能理解是一个巨大的挑战。基于网络的OMIC数据分析已经确定了与疾病相关基因的模块，这些模块既用于获得系统水平和对疾病机制的分子理解。例如，在过敏中，一个模块用于找到一个新的候选基因，该基因通过功能和临床研究验证。这样的分析在系统医学中起着重要作用。这是一门新兴学科，旨在获得对共同疾病潜在的复杂机制的翻译理解。在这篇综述中，我们将解释并提供示例，说明基于网络的OMIC数据分析如何结合功能和临床研究，从而有助于我们对疾病的理解，并有助于确定诊断标记或治疗性候选基因的优先级。这些分析涉及重大问题和局限性，将进行讨论。我们还强调了临床实施所需的步骤。
Many common diseases, such as asthma, diabetes or obesity, involve altered interactions between thousands of genes. High-throughput techniques (omics) allow identification of such genes and their products, but functional understanding is a formidable challenge. Network-based analyses of omics data have identified modules of disease-associated genes that have been used to obtain both a systems level and a molecular understanding of disease mechanisms. For example, in allergy a module was used to find a novel candidate gene that was validated by functional and clinical studies. Such analyses play important roles in systems medicine. This is an emerging discipline that aims to gain a translational understanding of the complex mechanisms underlying common diseases. In this review, we will explain and provide examples of how network-based analyses of omics data, in combination with functional and clinical studies, are aiding our understanding of disease, as well as helping to prioritize diagnostic markers or therapeutic candidate genes. Such analyses involve significant problems and limitations, which will be discussed. We also highlight the steps needed for clinical implementation.
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