1 citations · 1 across the 5 of their papers we have counts for
5 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…
Trade-offs between cost and information in cellular prediction
Age J. Tjalma, Vahe Galstyan, Jeroen Goedhart +3
Living cells can leverage correlations in environmental fluctuations to predict the future environment and mount a response ahead of time. To this end, cells need to encode the pas…
Estimation of Predictive Performance in High-Dimensional Data Settings using Learning Curves
Jeroen M. Goedhart, Thomas Klausch, Mark A. van de Wiel
In high-dimensional prediction settings, it remains challenging to reliably estimate the test performance. To address this challenge, a novel performance estimation framework is pr…