ArticleScientific reports2015
Application of high-dimensional feature selection: evaluation for genomic prediction in man.
Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 85 papers, 1 of them a synthesis that pooled it.
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Who cites it
85 citing papers in PubMed, 1 synthesis or guideline pooled it, 331 citations in OpenAlex.
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- Benchmarking Feature Selection Methods and Prediction Models for Flowering Time Prediction in Maize.International journal of molecular sciences · 2026Article
- MoEGP: an efficient crop genomic prediction approach based on the mixture of experts network.Plant methods · 2026Article
- Advancements in Animal Breeding: From Mendelian Genetics to Machine Learning.International journal of molecular sciences · 2025Review
- Machine learning for genomic prediction of growth traits in aquaculture: a case study of the Australasian snapper (Chrysophrys auratus).BMC bioinformatics · 2025Article
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- Monitoring pilots' mental workload in real flight conditions using multinomial logistic regression with a ridge estimator.Frontiers in robotics and AI · 2025Article
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- Reservoir temperature prediction based on characterization of water chemistry data-case study of western Anatolia, Turkey.Scientific reports · 2024Article
- Enhancing prediction accuracy of coronary artery disease through machine learning-driven genomic variant selection.Journal of translational medicine · 2024Article
- Machine learning for microbiologists.Nature reviews. Microbiology · 2024Review
- Selective Genotyping and Phenotyping for Optimization of Genomic Prediction Models for Populations with Different Diversity.Plants (Basel, Switzerland) · 2024Article
- An enhanced and efficient approach for feature selection for chronic human disease prediction: A breast cancer study.Heliyon · 2024Article
25 more citing papers are in PubMed but not listed here.
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Authors and funding
11 authors at 3 institutions in 1 country.
Funding
Abstract
In this study, we investigated the effect of five feature selection approaches on the performance of a mixed model (G-BLUP) and a Bayesian (Bayes C) prediction method. We predicted height, high density lipoprotein cholesterol (HDL) and body mass index (BMI) within 2,186 Croatian and into 810 UK individuals using genome-wide SNP data. Using all SNP information Bayes C and G-BLUP had similar predictive performance across all traits within the Croatian data, and for the highly polygenic traits height and BMI when predicting into the UK data. Bayes C outperformed G-BLUP in the prediction of HDL, which is influenced by loci of moderate size, in the UK data. Supervised feature selection of a SNP subset in the G-BLUP framework provided a flexible, generalisable and computationally efficient alternative to Bayes C; but careful evaluation of predictive performance is required when supervised feature selection has been used.
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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.