Mit Big Data zu einer gezielteren Diabetes-Prävention und -Therapie
Author:
Publisher
Springer Science and Business Media LLC
Subject
Endocrinology, Diabetes and Metabolism
Link
https://link.springer.com/content/pdf/10.1007/s11428-021-00827-8.pdf
Reference9 articles.
1. Ahlkvist E et al (2018) Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables. Lancet Diabetes Endocrinol 6(5):361–369. https://doi.org/10.1016/S2213-8587(18)30051-2
2. Zaharia O et al (2019) Risk of diabetes-associated diseases in subgroups of patients with recent-onset diabetes: a 5-year follow-up study. Lancet Diabetes Endocrinol 7(9):684–694. https://doi.org/10.1016/s2213-8587(19)30187-1
3. Wagner R et al (2021) Pathophysiology-based subphenotyping of individuals at elevated risk for type 2 diabetes. Nat Med 27:49–57. https://doi.org/10.1038/s41591-020-1116-9
4. Eulenberg P et al (2017) Reconstructing cell cycle and disease progression using deep learning. Nat Commun 8:463. https://doi.org/10.1038/s41467-017-00623-3
5. Winkler C et al (2014) Feature ranking of type 1 diabetes susceptibility genes improves prediction of type 1 diabetes. Diabetologia 57:2521–2529
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