# Penalised regression with multiple sources of prior effects

arXiv: https://arxiv.org/abs/2212.08581 · HTML: https://mlsanity.com/paper/2212.08581/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 16 Dec 2022.

## Builds on (most cited first, 5 of 5)

- [Transfer Learning under High-dimensional Generalized Linear Models](https://mlsanity.com/paper/2105.14328/) (2021)
- [Better prediction by use of co-data: Adaptive group-regularized ridge regression](https://mlsanity.com/paper/1411.3496/) (2014)
- [High-Dimensional Regression with Binary Coefficients. Estimating Squared Error and a Phase Transition](https://mlsanity.com/paper/1701.04455/) (2017)
- [Feature-weighted elastic net: using "features of features" for better prediction](https://mlsanity.com/paper/2006.01395/) (2020)
- [Flexible co-data learning for high-dimensional prediction](https://mlsanity.com/paper/2005.04010/) (2020)

## Abstract (arXiv)

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).