AUC-RF: A New Strategy for Genomic Profiling with Random Forest
M. Luz Calle(Universitat de Vic - Universitat Central de Catalunya), Núria Malats(Spanish National Cancer Research Centre), Anne‐Laure Boulesteix(Zimmer Biomet (Netherlands)), Víctor Urrea(IrsiCaixa)
Cited by 146
Related Papers
Bias in random forest variable importance measures: Illustrations, sources and a solution
|BMC Bioinformatics|2007|3.7k
Conditional variable importance for random forests
|BMC Bioinformatics|2008|3.3k
TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods
|BMJ|2024|2.8k
A Consensus Molecular Classification of Muscle-invasive Bladder Cancer
|European Urology|2019|1.4k
Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics
|Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery|2012|937