# Guiding adaptive shrinkage by co-data to improve regression-based prediction and feature selection

arXiv: https://arxiv.org/abs/2405.04917 · HTML: https://mlsanity.com/paper/2405.04917/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 38 references that are in the map.
- First posted on arXiv 8 May 2024.

## Builds on (most cited first, 11 of 11)

- [Better prediction by use of co-data: Adaptive group-regularized ridge regression](https://mlsanity.com/paper/1411.3496/) (2014)
- [Feature-weighted elastic net: using "features of features" for better prediction](https://mlsanity.com/paper/2006.01395/) (2020)
- [Adaptive penalization in high-dimensional regression and classification with external covariates using variational Bayes](https://mlsanity.com/paper/1811.02962/) (2018)
- [Flexible co-data learning for high-dimensional prediction](https://mlsanity.com/paper/2005.04010/) (2020)
- [Adaptive group-regularized logistic elastic net regression](https://mlsanity.com/paper/1805.00389/) (2018)
- [Fast marginal likelihood estimation of penalties for group-adaptive elastic net](https://mlsanity.com/paper/2101.03875/) (2021)
- [Structured penalized regression for drug sensitivity prediction](https://mlsanity.com/paper/1902.04996/) (2019)
- [σ-Ridge: group regularized ridge regression via empirical Bayes noise level cross-validation](https://mlsanity.com/paper/2010.15817/) (2020)
- [ecpc: An R-package for generic co-data models for high-dimensional prediction](https://mlsanity.com/paper/2205.07640/) (2022)
- [Informative co-data learning for high-dimensional Horseshoe regression](https://mlsanity.com/paper/2303.05898/) (2023)
- [Learning from a lot: Empirical Bayes in high-dimensional prediction settings](https://mlsanity.com/paper/1709.04192/) (2017)

## Abstract (arXiv)

The high dimensional nature of genomics data complicates feature selection, in particular in low sample size studies - not uncommon in clinical prediction settings. It is widely recognized that complementary data on the features, `co-data', may improve results. Examples are prior feature groups or p-values from a related study. Such co-data are ubiquitous in genomics settings due to the availability of public repositories. Yet, the uptake of learning methods that structurally use such co-data is limited. We review guided adaptive shrinkage methods: a class of regression-based learners that use co-data to adapt the shrinkage parameters, crucial for the performance of those learners. We discuss technical aspects, but also the applicability in terms of types of co-data that can be handled. This class of methods is contrasted with several others. In particular, group-adaptive shrinkage is compared with the better-known sparse group-lasso by evaluating feature selection. Finally, we demonstrate the versatility of the guided shrinkage methodology by showing how to `do-it-yourself': we integrate implementations of a co-data learner and the spike-and-slab prior for the purpose of improving feature selection in genetics studies.