ArticleBiostatistics (Oxford, England)2021
Adaptive penalization in high-dimensional regression and classification with external covariates using variational Bayes.
Article in Biostatistics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Low-Rank Variational Correction Estimation for Multi-Source Heterogeneous Quantile Linear Regression Models.Statistics in medicine · 2026Article
- Enhancing cancer prognostics with group penalty models: a comparative study on radiomics feature selection in lung adenocarcinomas and meningiomas.Quantitative imaging in medicine and surgery · 2026Article
- Adaptive Use of Co-Data Through Empirical Bayes for Bayesian Additive Regression Trees.Statistics in medicine · 2025Article
- A probabilistic modeling framework for genomic networks incorporating sample heterogeneity.Cell reports methods · 2025Article
- Functional Integrative Bayesian Analysis of High-dimensional Multiplatform Clinicogenomic Data.Journal of the American Statistical Association · 2024Article
- ecpc: an R-package for generic co-data models for high-dimensional prediction.BMC bioinformatics · 2023Article
- Feature-weighted elastic net: using "features of features" for better prediction.Statistica Sinica · 2023Article
- Fast Marginal Likelihood Estimation of Penalties for Group-Adaptive Elastic Net.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2023Article
- Flexible co-data learning for high-dimensional prediction.Statistics in medicine · 2021Article
- A Detailed Catalogue of Multi-Omics Methodologies for Identification of Putative Biomarkers and Causal Molecular Networks in Translational Cancer Research.International journal of molecular sciences · 2021Review
- Integration of mechanistic immunological knowledge into a machine learning pipeline improves predictions.Nature machine intelligence · 2020Article
- Design and Rationale of the ERA-CVD Consortium PREMED-CAD-Precision Medicine in Coronary Artery Disease.Biomolecules · 2020Article
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2 authors.
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Abstract
Penalization schemes like Lasso or ridge regression are routinely used to regress a response of interest on a high-dimensional set of potential predictors. Despite being decisive, the question of the relative strength of penalization is often glossed over and only implicitly determined by the scale of individual predictors. At the same time, additional information on the predictors is available in many applications but left unused. Here, we propose to make use of such external covariates to adapt the penalization in a data-driven manner. We present a method that differentially penalizes feature groups defined by the covariates and adapts the relative strength of penalization to the information content of each group. Using techniques from the Bayesian tool-set our procedure combines shrinkage with feature selection and provides a scalable optimization scheme. We demonstrate in simulations that the method accurately recovers the true effect sizes and sparsity patterns per feature group. Furthermore, it leads to an improved prediction performance in situations where the groups have strong differences in dynamic range. In applications to data from high-throughput biology, the method enables re-weighting the importance of feature groups from different assays. Overall, using available covariates extends the range of applications of penalized regression, improves model interpretability and can improve prediction performance.
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