Reference : Penalised regression with multiple sources of prior effects

	Document type :	E-prints/Working papers : Already available on another site
	Discipline(s) :	Physical, chemical, mathematical & earth Sciences : Mathematics
	Focus Areas :	Systems Biomedicine
	To cite this reference:	http://hdl.handle.net/10993/53227

Title :	Penalised regression with multiple sources of prior effects
Language :	English
Author, co-author :	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 >]
Publication date :	2022
Peer reviewed :	No
Keywords :	[en] transfer learning ; co-data ; prior information ; ridge regression
Abstract :	[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).
Research centres :	Luxembourg Centre for Systems Biomedicine (LCSB): Biomedical Data Science (Glaab Group)
Funders :	Fonds National de la Recherche - FnR
Name of the research project :	DIGIPD > Validating Digital Biomarkers For Better Personalized Treatment Of Parkinson’s Disease > 01/05/2021 > 30/04/2024 > 2020
Target :	Researchers ; Professionals ; Students
Permalink :	http://hdl.handle.net/10993/53227
DOI :	10.48550/arXiv.2212.08581
source URL :	https://doi.org/10.48550/arXiv.2212.08581
Framework Programme / European Project :	H2020 ; 779282 - ERA PerMed - ERA-Net Cofund in Personalised Medicine
FnR project :	FnR ; FNR14599012 > Enrico Glaab > DIGIPD > Validating Digital Biomarkers For Better Personalized Treatment Of Parkinson’S Disease > 01/05/2021 > 30/04/2024 > 2020

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	Fulltext file(s): File Commentary Version Size Access Open access transreg.pdf Author preprint 971.66 kB View/Open

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