The future is now? Clinical and translational aspects of “Omics” technologies,Immunology and Cell Biology

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The future is now? Clinical and translational aspects of “Omics” technologies
Immunology and Cell Biology ( IF 3.2 ) Pub Date : 2020-09-13 , DOI: 10.1111/imcb.12404
Gemma L D'Adamo ₁ , James T Widdop ₁ , Edward M Giles _{1,

2}

Affiliation

Big data has become a central part of medical research, as well as modern life generally. “Omics” technologies include genomics, proteomics, microbiomics and increasingly other omics. These have been driven by rapid advances in laboratory techniques and equipment. Crucially, improved information handling capabilities have allowed concepts such as artificial intelligence and machine learning to enter the research world. The COVID‐19 pandemic has shown how quickly information can be generated and analyzed using such approaches, but also showed its limitations. This review will look at how “omics” has begun to be translated into clinical practice. While there appears almost limitless potential in using big data for “precision” or “personalized” medicine, the reality is that this remains largely aspirational. Oncology is the only field of medicine that is widely adopting such technologies, and even in this field uptake is irregular. There are practical and ethical reasons for this lack of translation of increasingly affordable techniques into the clinic. Undoubtedly, there will be increasing use of large data sets from traditional (e.g. tumor samples, patient genomics) and nontraditional (e.g. smartphone) sources. It is perhaps the greatest challenge of the health‐care sector over the coming decade to integrate these resources in an effective, practical and ethical way.

中文翻译：

未来是现在？“ Omics”技术的临床和翻译方面

大数据已经成为医学研究以及整个现代生活的核心部分。“组学”技术包括基因组学，蛋白质组学，微生物组学以及越来越多的其他组学技术。这些是由实验室技术和设备的快速发展所推动的。至关重要的是，改进的信息处理能力使诸如人工智能和机器学习之类的概念进入了研究领域。COVID-19大流行表明了使用这种方法可以多快地生成和分析信息，但也显示了其局限性。本文将探讨“组学”如何开始转化为临床实践。尽管使用大数据进行“精确”或“个性化”医学的潜力几乎无限，但现实情况是，这仍然是理想中的。肿瘤学是唯一广泛采用此类技术的医学领域，即使在这一领域，吸收也是不规则的。缺乏将越来越多的负担得起的技术转化为临床的实际和伦理原因。无疑，将越来越多地使用来自传统（例如肿瘤样本，患者基因组学）和非传统（例如智能手机）来源的大数据集。在未来十年中，以有效，实用和合乎道德的方式整合这些资源可能是卫生保健部门面临的最大挑战。G。肿瘤样本，患者基因组）和非传统来源（例如智能手机）。在未来十年中，以有效，实用和合乎道德的方式整合这些资源可能是卫生保健部门面临的最大挑战。G。肿瘤样本，患者基因组）和非传统来源（例如智能手机）。在未来十年中，以有效，实用和合乎道德的方式整合这些资源可能是卫生保健部门面临的最大挑战。

更新日期：2020-09-13

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