ArticlePloS one2015
A Ranking Approach to Genomic Selection.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.
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Who cites it
35 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Genome-Enabled Prediction Methods Based on Machine Learning.Methods in molecular biology (Clifton, N.J.) · 2022Pooled it
- Genomic prediction in quinoa across contrasting environments using statistical and machine learning models.The plant genome · 2026Article
- From linear models to deep learning: statistical advances in genomic selection for animal breeding.Journal of animal science and biotechnology · 2026Review
- Multi-metric evaluation and parametric optimization of stochastic gradient boosting machines for genomic prediction and selection in wheat (Triticum aestivum) breeding.G3 (Bethesda, Md.) · 2026Article
- Optimizing training sets for genomic selection to identify superior genotypes across multiple environments.G3 (Bethesda, Md.) · 2026Article
- ReaGP: integrating residual units and attention mechanisms in convolution neural network for genomic prediction.Genetics, selection, evolution : GSE · 2026Article
- Normalized cumulative gain as an alternative evaluation measure for genomic selection models.Genetics, selection, evolution : GSE · 2025Article
- Optimizing training sets to identify superior genotypes in hybrid populations.Frontiers in plant science · 2025Article
- Constructing training sets for genomic selection to identify superior genotypes in candidate populations.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- Machine learning for genomic and pedigree prediction in sugarcane.The plant genome · 2024Article
- Review of applications of artificial intelligence (AI) methods in crop research.Journal of applied genetics · 2024Review
- Genomic prediction for sugarcane diseases including hybrid Bayesian-machine learning approaches.Frontiers in plant science · 2024Article
- Using the Pearson's correlation coefficient as the sole metric to measure the accuracy of quantitative trait prediction: is it sufficient?Frontiers in plant science · 2024Article
- PNNGS, a multi-convolutional parallel neural network for genomic selection.Frontiers in plant science · 2024Article
- Evaluation of eight Bayesian genomic prediction models for three micronutrient traits in bread wheat (Triticum aestivum L.).The plant genome · 2023Article
- Exploring the potential of incremental feature selection to improve genomic prediction accuracy.Genetics, selection, evolution : GSE · 2023Article
- Realized genomic selection across generations in a reciprocal recurrent selection breeding program ofFrontiers in plant science · 2023Article
- G2P Provides an Integrative Environment for Multi-model genomic selection analysis to improve genotype-to-phenotype prediction.Frontiers in plant science · 2023Article
- IP4GS: Bringing genomic selection analysis to breeders.Frontiers in plant science · 2023Article
- Stacked kinship CNN vs. GBLUP for genomic predictions of additive and complex continuous phenotypes.Scientific reports · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGenomic selection (GS) is a recent selective breeding method which uses predictive models based on whole-genome molecular markers. Until now, existing studies formulated GS as the problem of modeling an individual's breeding value for a particular trait of interest, i.e., as a regression problem. To assess predictive accuracy of the model, the Pearson correlation between observed and predicted trait values was used. CONTRIBUTIONS: In this paper, we propose to formulate GS as the problem of ranking individuals according to their breeding value. Our proposed framework allows us to employ machine learning methods for ranking which had previously not been considered in the GS literature. To assess ranking accuracy of a model, we introduce a new measure originating from the information retrieval literature called normalized discounted cumulative gain (NDCG). NDCG rewards more strongly models which assign a high rank to individuals with high breeding value. Therefore, NDCG reflects a prerequisite objective in selective breeding: accurate selection of individuals with high breeding value.
resultsWe conducted a comparison of 10 existing regression methods and 3 new ranking methods on 6 datasets, consisting of 4 plant species and 25 traits. Our experimental results suggest that tree-based ensemble methods including McRank, Random Forests and Gradient Boosting Regression Trees achieve excellent ranking accuracy. RKHS regression and RankSVM also achieve good accuracy when used with an RBF kernel. Traditional regression methods such as Bayesian lasso, wBSR and BayesC were found less suitable for ranking. Pearson correlation was found to correlate poorly with NDCG. Our study suggests two important messages. First, ranking methods are a promising research direction in GS. Second, NDCG can be a useful evaluation measure for GS.
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