ArticleBiostatistics (Oxford, England)2021
Adaptive group-regularized logistic elastic net regression.
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, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Leveraging pleiotropic association using sparse group variable selection in genomics data.BMC medical research methodology · 2022Pooled it
- Artificial intelligence to investigate metabolomics data for precision medicine.Metabolomics : Official journal of the Metabolomic Society · 2026Review
- Machine learning for prediction of in-ICU mortality in sepsis: an observational study using the MIMIC IV database.Archives of medical science : AMS · 2026Article
- MMFSyn: A Multimodal Deep Learning Model for Predicting Anticancer Synergistic Drug Combination Effect.Biomolecules · 2024Article
- Predictive maintenance in Industry 4.0: a survey of planning models and machine learning techniques.PeerJ. Computer science · 2024Article
- Predicting implementation of response to intervention in math using elastic net logistic regression.Frontiers in psychology · 2024Article
- Machine learning predictive model for aspiration screening in hospitalized patients with acute stroke.Scientific reports · 2023Article
- ecpc: an R-package for generic co-data models for high-dimensional prediction.BMC bioinformatics · 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
- Semi-supervised empirical Bayes group-regularized factor regression.Biometrical journal. Biometrische Zeitschrift · 2022Article
- Flexible co-data learning for high-dimensional prediction.Statistics in medicine · 2021Article
- Article
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4 authors.
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Abstract
In high-dimensional data settings, additional information on the features is often available. Examples of such external information in omics research are: (i) $p$-values from a previous study and (ii) omics annotation. The inclusion of this information in the analysis may enhance classification performance and feature selection but is not straightforward. We propose a group-regularized (logistic) elastic net regression method, where each penalty parameter corresponds to a group of features based on the external information. The method, termed gren, makes use of the Bayesian formulation of logistic elastic net regression to estimate both the model and penalty parameters in an approximate empirical-variational Bayes framework. Simulations and applications to three cancer genomics studies and one Alzheimer metabolomics study show that, if the partitioning of the features is informative, classification performance, and feature selection are indeed enhanced.
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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.