5 citations · 6 across the 2 of their papers we have counts for
5 papers
ecpc: An R-package for generic co-data models for high-dimensional prediction
Mirrelijn M. van Nee, Lodewyk F. A. Wessels, Mark A. van de Wiel
High-dimensional prediction considers data with more variables than samples. Generic research goals are to find the best predictor or to select variables. Results may be improved b…
Flexible co-data learning for high-dimensional prediction
Mirrelijn M. van Nee, Lodewyk F. A. Wessels, Mark A. van de Wiel
Clinical research often focuses on complex traits in which many variables play a role in mechanisms driving, or curing, diseases. Clinical prediction is hard when data is high-dime…
Heterofusion: Fusing genomics data of different measurement scales
Age K. Smilde, Yipeng Song, Johan A. Westerhuis +3
In systems biology, it is becoming increasingly common to measure biochemical entities at different levels of the same biological system. Hence, data fusion problems are abundant i…
Generalized simultaneous component analysis of binary and quantitative data
Yipeng Song, Johan A. Westerhuis, Nanne Aben +3
In the current era of systems biological research there is a need for the integrative analysis of binary and quantitative genomics data sets measured on the same objects. One stand…
Approximating multivariate posterior distribution functions from Monte Carlo samples for sequential Bayesian inference
Bram Thijssen, Lodewyk F. A. Wessels
An important feature of Bayesian statistics is the opportunity to do sequential inference: the posterior distribution obtained after seeing a dataset can be used as prior for a sec…