ArticleGenetic epidemiology2013
Ridge regression in prediction problems: automatic choice of the ridge parameter.
Article in Genetic epidemiology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed.
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- AI-derived 3D body composition predicts prognosis of chemoimmunotherapy in small cell lung cancer: a multicenter study.Journal for immunotherapy of cancer · 2026Article
- Minimizing Stochastic Complexity with Ridge Regression.Entropy (Basel, Switzerland) · 2026Article
- A dual track predictive model for assessing crow's feet aging across different clinical severity grades.Scientific reports · 2026Article
- Spectral and non-spectral EEG measures in the prediction of working memory task performance and psychopathology.bioRxiv : the preprint server for biology · 2026Article
- Sensorimotor Parameters Predict Performance on the Bead Maze Hand Function Test.Sensors (Basel, Switzerland) · 2025Article
- Lagged precipitation effects on plant production across terrestrial biomes.Nature ecology & evolution · 2025Article
- Evaluating Genetic Regulators of MicroRNAs Using Machine Learning Models.International journal of molecular sciences · 2025Article
- Reduced myelin contributes to cognitive impairment in patients with monogenic small vessel disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Terminal differentiation and persistence of effector regulatory T cells essential for preventing intestinal inflammation.Nature immunology · 2025Article
- Prediction of inhibitory peptides against E.coli with desired MIC value.Scientific reports · 2025Article
- Methodologies underpinning polygenic risk scores estimation: a comprehensive overview.Human genetics · 2024Review
- Exploring the Associations of Inflammatory and Oxidative Stress Biomarkers with Pancreatic Diseases: An Observational and Mendelian Randomisation Study.Journal of clinical medicine · 2024Article
- Predictors of mental health problems during the COVID-19 outbreak in Egypt in 2021.Frontiers in public health · 2023Article
- Effect of imbalance in dietary macronutrients on blood hemoglobin levels: a cross-sectional study in young underweight Japanese women.Frontiers in nutrition · 2023Article
- Multivariate Sequential Analytics for Cardiovascular Disease Event Prediction.Methods of information in medicine · 2022Article
- Traits, phylogeny and host cell receptors predict Ebolavirus host status among African mammals.PLoS neglected tropical diseases · 2022Article
- Medication adherence prediction through temporal modelling in cardiovascular disease management.BMC medical informatics and decision making · 2022Article
- A risk calculator to predict suicide attempts among individuals with early-onset bipolar disorder.Bipolar disorders · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To date, numerous genetic variants have been identified as associated with diverse phenotypic traits. However, identified associations generally explain only a small proportion of trait heritability and the predictive power of models incorporating only known-associated variants has been small. Multiple regression is a popular framework in which to consider the joint effect of many genetic variants simultaneously. Ordinary multiple regression is seldom appropriate in the context of genetic data, due to the high dimensionality of the data and the correlation structure among the predictors. There has been a resurgence of interest in the use of penalised regression techniques to circumvent these difficulties. In this paper, we focus on ridge regression, a penalised regression approach that has been shown to offer good performance in multivariate prediction problems. One challenge in the application of ridge regression is the choice of the ridge parameter that controls the amount of shrinkage of the regression coefficients. We present a method to determine the ridge parameter based on the data, with the aim of good performance in high-dimensional prediction problems. We establish a theoretical justification for our approach, and demonstrate its performance on simulated genetic data and on a real data example. Fitting a ridge regression model to hundreds of thousands to millions of genetic variants simultaneously presents computational challenges. We have developed an R package, ridge, which addresses these issues. Ridge implements the automatic choice of ridge parameter presented in this paper, and is freely available from CRAN.
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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.