Improving cross-study prediction through addon batch effect adjustment or addon normalization
Roman Hornung(Zimmer Biomet (Netherlands)), Anne‐Laure Boulesteix(Zimmer Biomet (Netherlands)), David Causeur(Institut Agro Rennes-Angers), Christoph Bernau(Boston University)
Cited by 42
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
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
Random forest versus logistic regression: a large-scale benchmark experiment
|BMC Bioinformatics|2018|882