RWTH Aachen University, Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE), Aachen, Germany; Institute for Computational Biomedicine, Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Heidelberg, Germany; Molecular Medicine Partnership Unit (MMPU), European Molecular Biology Laboratory and Heidelberg University, Heidelberg, Germany.
Department II of Internal Medicine, and Center for Molecular Medicine Cologne, University of Cologne, Cologne, Germany; Center for Mass Spectrometry and Metabolomics, The Scripps Research Institute, La Jolla, California, USA.
Kidney Int. 2019 Jun;95(6):1326-1337. doi: 10.1016/j.kint.2018.11.048. Epub 2019 Mar 5.
There have been tremendous advances during the last decade in methods for large-scale, high-throughput data generation and in novel computational approaches to analyze these datasets. These advances have had a profound impact on biomedical research and clinical medicine. The field of genomics is rapidly developing toward single-cell analysis, and major advances in proteomics and metabolomics have been made in recent years. The developments on wearables and electronic health records are poised to change clinical trial design. This rise of 'big data' holds the promise to transform not only research progress, but also clinical decision making towards precision medicine. To have a true impact, it requires integrative and multi-disciplinary approaches that blend experimental, clinical and computational expertise across multiple institutions. Cancer research has been at the forefront of the progress in such large-scale initiatives, so-called 'big science,' with an emphasis on precision medicine, and various other areas are quickly catching up. Nephrology is arguably lagging behind, and hence these are exciting times to start (or redirect) a research career to leverage these developments in nephrology. In this review, we summarize advances in big data generation, computational analysis, and big science initiatives, with a special focus on applications to nephrology.
在过去十年中，大规模、高通量数据生成的方法以及分析这些数据集的新计算方法取得了巨大进展。这些进展对生物医学研究和临床医学产生了深远的影响。基因组学领域正迅速向单细胞分析发展，近年来蛋白质组学和代谢组学也取得了重大进展。可穿戴设备和电子健康记录的发展有望改变临床试验设计。“大数据”的兴起有望不仅改变研究进展，而且朝着精准医学的方向改变临床决策。要产生真正的影响，需要整合跨多个机构的实验、临床和计算专业知识的多学科方法。癌症研究一直处于此类大规模计划（所谓的“大科学”）的前沿，重点是精准医学，其他各个领域也在迅速跟进。肾脏病学可以说是落后了，因此，现在是开始（或重新开始）从事肾脏病学研究职业以利用这些发展的令人兴奋的时刻。在这篇综述中，我们总结了大数据生成、计算分析和大科学计划的进展，特别关注其在肾脏病学中的应用。
