Penalised regression with multiple sources of prior effects
English
Rauschenberger, Armin[University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Biomedical Data Science >]
Landoulsi, Zied[University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Bioinformatics Core >]
van de Wiel, Mark A.[Amsterdam University Medical Centers > Epidemiology and Data Science > > ; University of Cambridge > Medical Research Council Biostatistics Unit]
Glaab, Enrico[University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Biomedical Data Science >]
2022
No
[en] transfer learning ; co-data ; prior information ; ridge regression
[en] In many high-dimensional prediction or classification tasks, complementary data on the features are available, e.g. prior biological knowledge on (epi)genetic markers. Here we consider tasks with numerical prior information that provide an insight into the importance (weight) and the direction (sign) of the feature effects, e.g. regression coefficients from previous studies. We propose an approach for integrating multiple sources of such prior information into penalised regression. If suitable co-data are available, this improves the predictive performance, as shown by simulation and application. The proposed method is implemented in the R package `transreg' (https://github.com/lcsb-bds/transreg).
Luxembourg Centre for Systems Biomedicine (LCSB): Biomedical Data Science (Glaab Group)
Fonds National de la Recherche - FnR
DIGIPD > Validating Digital Biomarkers For Better Personalized Treatment Of Parkinson’s Disease > 01/05/2021 > 30/04/2024 > 2020