1 citations · 1 across the 2 of their papers we have counts for
3 papers
How important are the genes to explain the outcome - the asymmetric Shapley value as an honest importance metric for high-dimensional features
Mark A. van de Wiel, Jeroen Goedhart, Martin Jullum +1
In clinical prediction settings the importance of a high-dimensional feature like genomics is often assessed by evaluating the change in predictive performance when adding it to a…
Fusion of Tree-induced Regressions for Clinico-genomic Data
Jeroen M. Goedhart, Mark A. van de Wiel, Wessel N. van Wieringen +1
Cancer prognosis is often based on a set of omics covariates and a set of established clinical covariates such as age and tumor stage. Combining these two sets poses challenges. Fi…
Co-data Learning for Bayesian Additive Regression Trees
Jeroen M. Goedhart, Thomas Klausch, Jurriaan Janssen +1
Medical prediction applications often need to deal with small sample sizes compared to the number of covariates. Such data pose problems for prediction and variable selection, espe…