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将生物信息学应用于系统医学。

Taking Bioinformatics to Systems Medicine.

作者信息

van Kampen Antoine H C, Moerland Perry D

机构信息

Bioinformatics Laboratory, Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Center (AMC), University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands.

Biosystems Data Analysis, Swammerdam Institute for Life Sciences, University of Amsterdam, Science Park 904, 1098 XH, Amsterdam, The Netherlands.

出版信息

Methods Mol Biol. 2016;1386:17-41. doi: 10.1007/978-1-4939-3283-2_2.

Abstract

Systems medicine promotes a range of approaches and strategies to study human health and disease at a systems level with the aim of improving the overall well-being of (healthy) individuals, and preventing, diagnosing, or curing disease. In this chapter we discuss how bioinformatics critically contributes to systems medicine. First, we explain the role of bioinformatics in the management and analysis of data. In particular we show the importance of publicly available biological and clinical repositories to support systems medicine studies. Second, we discuss how the integration and analysis of multiple types of omics data through integrative bioinformatics may facilitate the determination of more predictive and robust disease signatures, lead to a better understanding of (patho)physiological molecular mechanisms, and facilitate personalized medicine. Third, we focus on network analysis and discuss how gene networks can be constructed from omics data and how these networks can be decomposed into smaller modules. We discuss how the resulting modules can be used to generate experimentally testable hypotheses, provide insight into disease mechanisms, and lead to predictive models. Throughout, we provide several examples demonstrating how bioinformatics contributes to systems medicine and discuss future challenges in bioinformatics that need to be addressed to enable the advancement of systems medicine.

摘要

系统医学推动了一系列在系统层面研究人类健康和疾病的方法与策略,旨在改善(健康)个体的整体福祉,并预防、诊断或治愈疾病。在本章中,我们将讨论生物信息学如何对系统医学做出关键贡献。首先,我们解释生物信息学在数据管理和分析中的作用。特别是,我们展示了公开可用的生物和临床数据库对支持系统医学研究的重要性。其次,我们讨论如何通过整合生物信息学对多种组学数据进行整合和分析,这可能有助于确定更具预测性和稳健性的疾病特征,更好地理解(病理)生理分子机制,并推动个性化医疗。第三,我们专注于网络分析,讨论如何从组学数据构建基因网络,以及如何将这些网络分解为更小的模块。我们讨论了如何利用所得模块生成可实验验证的假设,深入了解疾病机制,并得出预测模型。在整个过程中,我们提供了几个例子来说明生物信息学如何对系统医学做出贡献,并讨论了生物信息学在推动系统医学发展方面需要应对的未来挑战。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/26a9/7120931/13e99b502038/321839_1_En_2_Fig1_HTML.jpg

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