Random forest-based modelling to detect biomarkers for prostate cancer progression
R. Toth, H. Schiffmann, C. Hube-Magg, F. Büscheck, D. Höflmayer, S. Weidemann, P. Lebok, C. Fraune, S. Minner, T. Schlomm, G. Sauter, C. Plass, Y. Assenov, R. Simon, J. Meiners, C. Gerhauser
This study uses a random forest model to identify DNA methylation biomarkers for prostate cancer progression, finding ZIC2 expression loss as an independent prognostic marker.
bioRxiv · Apr 8, 2019Cancer Biology