ArticleBioinformatics (Oxford, England)2021
Predictive and interpretable models via the stacked elastic net.
Article in Bioinformatics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Article
- Network-constrained Random Lasso for biologically interpretable gene network inference across unequal sample sizes.PloS one · 2026Article
- Interpretable multimodal machine learning for diagnosis of drug-resistant tuberculosis.Frontiers in digital health · 2026Article
- Fast and scalable ensemble learning method for versatile polygenic risk prediction.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Predicting dichotomised outcomes from high-dimensional data in biomedicine.Journal of applied statistics · 2024Article
- Penalized regression with multiple sources of prior effects.Bioinformatics (Oxford, England) · 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
- Ten quick tips for biomarker discovery and validation analyses using machine learning.PLoS computational biology · 2022Article
- Predicting correlated outcomes from molecular data.Bioinformatics (Oxford, England) · 2021Article
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Authors and funding
3 authors.
Funding
Abstract
motivationMachine learning in the biomedical sciences should ideally provide predictive and interpretable models. When predicting outcomes from clinical or molecular features, applied researchers often want to know which features have effects, whether these effects are positive or negative and how strong these effects are. Regression analysis includes this information in the coefficients but typically renders less predictive models than more advanced machine learning techniques.
resultsHere, we propose an interpretable meta-learning approach for high-dimensional regression. The elastic net provides a compromise between estimating weak effects for many features and strong effects for some features. It has a mixing parameter to weight between ridge and lasso regularization. Instead of selecting one weighting by tuning, we combine multiple weightings by stacking. We do this in a way that increases predictivity without sacrificing interpretability. AVAILABILITY AND IMPLEMENTATION: The R package starnet is available on GitHub (https://github.com/rauschenberger/starnet) and CRAN (https://CRAN.R-project.org/package=starnet).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.