A functional landscape of CKD entities from public transcriptomic data
To develop effective therapies and identify novel early biomarkers for chronic kidney disease, an understanding of the molecular mechanisms orchestrating it is essential. We here set out to understand how differences in chronic kidney disease (CKD) origin are reflected in gene expression. To this en...
Gespeichert in:
| Hauptverfasser: | , , , , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2020
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| In: |
Kidney international. Reports
Year: 2019, Jahrgang: 5, Heft: 2, Pages: 211-224 |
| ISSN: | 2468-0249 |
| DOI: | 10.1016/j.ekir.2019.11.005 |
| Online-Zugang: | Resolving-System, lizenzpflichtig, Volltext: https://doi.org/10.1016/j.ekir.2019.11.005 Verlag, lizenzpflichtig, Volltext: http://www.sciencedirect.com/science/article/pii/S2468024919315335 
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| Verfasserangaben: | Ferenc Tajti, Christoph Kuppe, Asier Antoranz, Mahmoud M. Ibrahim, Hyojin Kim, Francesco Ceccarelli, Christian H. Holland, Hannes Olauson, Jürgen Floege, Leonidas G. Alexopoulos, Rafael Kramann and Julio Saez-Rodriguez |
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| 520 | |a To develop effective therapies and identify novel early biomarkers for chronic kidney disease, an understanding of the molecular mechanisms orchestrating it is essential. We here set out to understand how differences in chronic kidney disease (CKD) origin are reflected in gene expression. To this end, we integrated publicly available human glomerular microarray gene expression data for 9 kidney disease entities that account for most of CKD worldwide. Our primary goal was to demonstrate the possibilities and potential on data analysis and integration to the nephrology community. | ||
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