ArticleJournal of animal science and biotechnology2017
Efficient strategies for leave-one-out cross validation for genomic best linear unbiased prediction.
Article in Journal of animal science and biotechnology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 117 papers, 3 of them syntheses that pooled it.
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117 citing papers in PubMed, 3 syntheses or guidelines pooled it.
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- Machine learning-based multimodal biomarkers enable accurate diagnosis and early detection of pancreatic ductal adenocarcinoma.Scientific reports · 2026Article
- Ventricular cardiac magnetic resonance traits and schizophrenia risk: a UK Biobank and Mendelian randomization study.Frontiers in psychiatry · 2026Article
- Altered Brain Function and Network Topology in Patients With Acromegaly: Resting-State fMRI Study of Networks Related to Cognitive and Emotional Processing.CNS neuroscience & therapeutics · 2026Article
- Association between blood lipids, BMI and gestational diabetes mellitus in early pregnancy: a clinical study and Mendelian randomization.Frontiers in endocrinology · 2026Article
- Prostate cancer diagnosis using sensitive and sophisticated machine learning classifiers based on non-invasive urinary RNA biomarkers (PCASSO).Scientific reports · 2025Article
- Causality between rheumatic diseases and iron-deficiency anemia: A 2-sample, 2-step mediation Mendelian randomization investigation.Medicine · 2025Article
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- Causal effects of cholecystectomy on the lipidome: cross-sectional and mendelian randomization studies.Surgical endoscopy · 2025Article
- Causal Effect of Blood Cell Perturbation Phenotypes on Multiple Sclerosis via Immune Mediation: A Mendelian Randomization Study.Cellular and molecular neurobiology · 2025Article
- Genetic Insights Into Skin Diseases and Depression: Evidence From East Asian Mendelian Randomization Analysis.Alpha psychiatry · 2025Article
57 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors.
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Abstract
backgroundA random multiple-regression model that simultaneously fit all allele substitution effects for additive markers or haplotypes as uncorrelated random effects was proposed for Best Linear Unbiased Prediction, using whole-genome data. Leave-one-out cross validation can be used to quantify the predictive ability of a statistical model.
methodsNaive application of Leave-one-out cross validation is computationally intensive because the training and validation analyses need to be repeated n times, once for each observation. Efficient Leave-one-out cross validation strategies are presented here, requiring little more effort than a single analysis.
resultsEfficient Leave-one-out cross validation strategies is 786 times faster than the naive application for a simulated dataset with 1,000 observations and 10,000 markers and 99 times faster with 1,000 observations and 100 markers. These efficiencies relative to the naive approach using the same model will increase with increases in the number of observations.
conclusionsEfficient Leave-one-out cross validation strategies are presented here, requiring little more effort than a single analysis.
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