Springer Nature
Browse
13148_2019_736_MOESM4_ESM.pdf (165.43 kB)

MOESM4 of Random forest-based modelling to detect biomarkers for prostate cancer progression

Download (165.43 kB)
journal contribution
posted on 2019-10-23, 08:24 authored by Reka Toth, Heiko Schiffmann, Claudia Hube-Magg, Franziska Bßscheck, Doris HÜflmayer, SÜren Weidemann, Patrick Lebok, Christoph Fraune, Sarah Minner, Thorsten Schlomm, Guido Sauter, Christoph Plass, Yassen Assenov, Ronald Simon, Jan Meiners, Clarissa Gerhäuser
Additional file 4: Figure S2. Performance of the random forest model. The plot shows the performance of the random forest model as a function of the trees built in the model, using the generalized OOB (black) and classification error for the good (red) and poor (green) prognosis groups.

Funding

Wilhelm Sander-Stiftung

History

Usage metrics

    Clinical Epigenetics

    Categories

    Keywords

    prostate cancer progressionforest modelRandom forest-based modellingMOESMOOB

    Licence

    CC BY + CC0

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC