  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).
