A framework to identify physiological responses in microarray-based gene expression studies: selection and interpretation of biologically relevant genes.

By W. Rodenburg, A. G. Heidema, J. Boer, I. Bovee-Oudenhoven, Edith J. M. Feskens, E. Mariman, J. Keijer, R. W., Heidema Ag, Boer Jm and 4 more. 2008

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Consistent Individualized Feature Attribution for Tree Ensembles (2018, methodology): “Different choices about the method of feature value permutation lead to variations of this basic approach [1, 10, 14, 23, 26].”
Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics (2012, methodology): “A simple graphical method which might help to identify predictors involved in interactions consists in plotting the RF VIMs (which may also capture interaction eﬀects) against a standard univariate statistic, see e.g. [61].”
A new variable importance measure for random forests with missing data (2012, background): “The work of Tang et al. (2009), Yang and Gu (2009), Rodenburg et al. (2008), Sandri and Zuccolotto (2006) and Díaz-Uriarte and Alvarez de Andrés (2006) shows that different approaches that are meant to improve prediction accuracy and select the most relevant variables have been developed.”