Penalized regression with multiple sources of prior effects

Author:

Rauschenberger Armin1ORCID,Landoulsi Zied1ORCID,van de Wiel Mark A2ORCID,Glaab Enrico1ORCID

Affiliation:

1. Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg , 4362 Esch-sur-Alzette, Luxembourg

2. Department of Epidemiology and Data Science (EDS), Amsterdam University Medical Centers (Amsterdam UMC) , 1081 HV Amsterdam, The Netherlands

Abstract

Abstract Motivation 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. Results We propose an approach for integrating multiple sources of such prior information into penalized regression. If suitable co-data are available, this improves the predictive performance, as shown by simulation and application. Availability and implementation The proposed method is implemented in the R package transreg (https://github.com/lcsb-bds/transreg, https://cran.r-project.org/package=transreg).

Funder

Luxembourg National Research Fund

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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