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.

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Keywords chronic kidney disease, gene expression, proteomic analysis
Persistent URL dx.doi.org/10.1016/j.kint.2018.11.048, hdl.handle.net/1765/117772
Journal Kidney International
Citation
Saez-Rodriguez, J., Rinschen, MM, Floege, J., & Kramann, R.J.T. (2019). Big science and big data in nephrology. Kidney International, 95(6), 1326–1337. doi:10.1016/j.kint.2018.11.048