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Interaction forests: Identifying and exploiting interpretable quantitative and qualitative interaction effects., and . Comput. Stat. Data Anal., (2022)Improving cross-study prediction through addon batch effect adjustment or addon normalization., , , and . Bioinform., 33 (3): 397-404 (2017)Priority-Lasso: a simple hierarchical approach to the prediction of clinical outcome using multi-omics data., , , , and . BMC Bioinform., 19 (1): 322:1-322:14 (2018)Combining location-and-scale batch effect adjustment with data cleaning by latent factor adjustment., , and . BMC Bioinform., (2016)Benchmark study of feature selection strategies for multi-omics data., , , and . BMC Bioinform., 23 (1): 412 (2022)Sequential Permutation Testing of Random Forest Variable Importance Measures., , and . CoRR, (2022)Large-scale benchmark study of survival prediction methods using multi-omics data., , , , and . Briefings Bioinform., (2021)Ordinal Forests.. J. Classif., 37 (1): 4-17 (2020)Evaluating machine learning models in non-standard settings: An overview and new findings., , , , , , , and . CoRR, (2023)Efficient permutation testing of variable importance measures by the example of random forests., , and . Comput. Stat. Data Anal., (May 2023)