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大数据在转化医学中的整合方法:单细胞和计算方法。

Approaches for the integration of big data in translational medicine: single-cell and computational methods.

机构信息

Genetics Division, Department of Cell and Molecular Biology and Microbiology, Faculty of Science and Technology, University of Isfahan, Isfahan, Iran.

Department of Anesthesiology, Faculty of Paramedical, Jahrom University of Medical Sciences, Jahrom, Iran.

出版信息

Ann N Y Acad Sci. 2021 Jun;1493(1):3-28. doi: 10.1111/nyas.14544. Epub 2021 Jan 6.

DOI:10.1111/nyas.14544
PMID:33410160
Abstract

Translational medicine describes a bench-to-bedside approach that eventually converts findings from basic scientific studies into real-world clinical research. It encompasses new treatments, advanced equipment, medical procedures, preventive and diagnostic approaches creating a bridge between basic studies and clinical research. Despite considerable investment in basic science, improvements in technology, and increased knowledge of the biology of human disease, translation of laboratory findings into substantial therapeutic progress has been slower than expected, and the return on investment has been limited in terms of clinical efficacy. In this review, we provide a fresh perspective on some experimental and computational approaches for translational medicine. We cover the analysis, visualization, and modeling of high-dimensional data, with a focus on single-cell technologies, sequence, and structure analysis. Current challenges, limitations, and future directions, with examples from cancer and fibrotic disease, will be discussed.

摘要

转化医学描述了一种从基础科学研究到实际临床研究的转化方法。它涵盖了新的治疗方法、先进的设备、医疗程序、预防和诊断方法,在基础研究和临床研究之间架起了桥梁。尽管在基础科学、技术进步和人类疾病生物学知识方面投入了大量资金,但将实验室研究成果转化为实质性治疗进展的速度比预期的要慢,投资回报率在临床疗效方面也受到了限制。在这篇综述中,我们提供了转化医学中一些实验和计算方法的新视角。我们涵盖了高维数据的分析、可视化和建模,重点是单细胞技术、序列和结构分析。将讨论当前的挑战、局限性和未来的方向,并以癌症和纤维化疾病为例进行说明。

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