Unbiased kidney-centric molecular categorization of chronic kidney disease as a step towards precision medicine.
Current classification of chronic kidney disease (CKD) into stages using indirect systemic measures (estimated glomerular filtration rate (eGFR) and albuminuria) is agnostic to the heterogeneity of underlying molecular processes in the kidney thereby limiting precision medicine approaches. To generate a novel CKD categorization that directly reflects within kidney disease drivers we analyzed publicly available transcriptomic data from kidney biopsy tissue. A Self-Organizing Maps unsupervised artificial neural network machine-learning algorithm was used to stratify a total of 369 patients with CKD and 46 living kidney donors as healthy controls.
Source: Kidney International - Category: Urology & Nephrology Authors: Anna Reznichenko, Viji Nair, Sean Eddy, Damian Fermin, Mark Tomilo, Timothy Slidel, Wenjun Ju, Ian Henry, Shawn S. Badal, Johnna D. Wesley, John T. Liles, Sven Moosmang, Julie M. Williams, Carol Moreno Quinn, Markus Bitzer, Jeffrey B. Hodgin, Laura Bariso Tags: clinical investigation Source Type: research