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[组学在精准医学时代的前景]

[The promise of omics in the precision medicine era].

作者信息

Tebani A, Bekri S

机构信息

UNIROUEN, Inserm U1245, Department of Metabolic Biochemistry, Normandie University, CHU Rouen, 76000 Rouen, France.

UNIROUEN, Inserm U1245, Department of Metabolic Biochemistry, Normandie University, CHU Rouen, 76000 Rouen, France.

出版信息

Rev Med Interne. 2022 Nov;43(11):649-660. doi: 10.1016/j.revmed.2022.07.009. Epub 2022 Aug 27.

Abstract

The rise of omics technologies that simultaneously measure thousands of molecules in a complex biological sample represents the core of systems biology. These technologies have profoundly impacted biomarkers and therapeutic targets discovery in the precision medicine era. Systems biology aims to perform a systematic probing of complex interactions in biological systems. Powered by high-throughput omics technologies and high-performance computing, systems biology provides relevant, resolving, and multi-scale overviews from cells to populations. Precision medicine takes advantage of these conceptual and technological developments and is based on two main pillars: the generation of multimodal data and their subsequent modeling. High-throughput omics technologies enable the comprehensive and holistic extraction of biological information, while computational capabilities enable multidimensional modeling and, as a result, offer an intuitive and intelligible visualization. Despite their promise, translating these technologies into clinically actionable tools has been slow. In this contribution, we present the most recent multi-omics data generation and analysis strategies and their clinical deployment in the post-genomic era. Furthermore, medical application challenges of omics-based biomarkers are discussed.

摘要

组学技术的兴起能够同时测量复杂生物样本中的数千种分子,这代表了系统生物学的核心。这些技术在精准医学时代对生物标志物和治疗靶点的发现产生了深远影响。系统生物学旨在对生物系统中的复杂相互作用进行系统探究。借助高通量组学技术和高性能计算,系统生物学提供了从细胞到群体的相关、解析性和多尺度概述。精准医学利用了这些概念和技术发展,基于两个主要支柱:多模态数据的生成及其后续建模。高通量组学技术能够全面、整体地提取生物信息,而计算能力则支持多维建模,从而提供直观且易懂的可视化。尽管这些技术前景广阔,但将其转化为临床可操作工具的进程却较为缓慢。在本论文中,我们展示了后基因组时代最新的多组学数据生成与分析策略及其临床应用。此外,还讨论了基于组学的生物标志物的医学应用挑战。

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