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精准医学中大数据分析的整合方法。

Integrative methods for analyzing big data in precision medicine.

作者信息

Gligorijević Vladimir, Malod-Dognin Noël, Pržulj Nataša

机构信息

Department of Computing, Imperial College London, London, UK.

出版信息

Proteomics. 2016 Mar;16(5):741-58. doi: 10.1002/pmic.201500396.

Abstract

We provide an overview of recent developments in big data analyses in the context of precision medicine and health informatics. With the advance in technologies capturing molecular and medical data, we entered the area of "Big Data" in biology and medicine. These data offer many opportunities to advance precision medicine. We outline key challenges in precision medicine and present recent advances in data integration-based methods to uncover personalized information from big data produced by various omics studies. We survey recent integrative methods for disease subtyping, biomarkers discovery, and drug repurposing, and list the tools that are available to domain scientists. Given the ever-growing nature of these big data, we highlight key issues that big data integration methods will face.

摘要

我们概述了在精准医学和健康信息学背景下大数据分析的最新进展。随着捕获分子和医学数据的技术进步,我们进入了生物医学领域的“大数据”时代。这些数据为推进精准医学提供了诸多机遇。我们概述了精准医学中的关键挑战,并介绍了基于数据整合的方法的最新进展,以从各种组学研究产生的大数据中发现个性化信息。我们调查了疾病亚型分类、生物标志物发现和药物再利用的最新整合方法,并列出了领域科学家可用的工具。鉴于这些大数据不断增长的特性,我们强调了大数据整合方法将面临的关键问题。

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