ArticleJournal of genetics2015
Evaluation of random forest regression for prediction of breeding value from genomewide SNPs.
Article in Journal of genetics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 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 vs. gene-based crop models: a case study on rice trait prediction.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Article
- Bioinformatics in crop research: using genomic data for crop improvement.Frontiers in plant science · 2026Review
- Performance Comparison of Genomic Best Linear Unbiased Prediction and Four Machine Learning Models for Estimating Genomic Breeding Values in Working Dogs.Animals : an open access journal from MDPI · 2025Article
- Incremental Inverse Design of Desired Soybean Phenotypes.ACS omega · 2024Article
- Genomic prediction in multi-environment trials in maize using statistical and machine learning methods.Scientific reports · 2024Article
- Phenomic Selection for Hybrid Rapeseed Breeding.Plant phenomics (Washington, D.C.) · 2024Article
- Transcriptome-wide association studies: recent advances in methods, applications and available databases.Communications biology · 2023Review
- Comparison of artificial intelligence algorithms and their ranking for the prediction of genetic merit in sheep.Scientific reports · 2022Article
- Comparing gradient boosting machine and Bayesian threshold BLUP for genome-based prediction of categorical traits in wheat breeding.The plant genome · 2022Article
- Deep polygenic neural network for predicting and identifying yield-associated genes in Indonesian rice accessions.Scientific reports · 2022Article
- Multi-tissue transcriptome-wide association studies.Genetic epidemiology · 2021Article
- A zero altered Poisson random forest model for genomic-enabled prediction.G3 (Bethesda, Md.) · 2021Article
- Article
- Investigation of prediction accuracy and the impact of sample size, ancestry, and tissue in transcriptome-wide association studies.Genetic epidemiology · 2020Article
- A Linear Regression Predictor for Identifying NMolecular therapy. Nucleic acids · 2019Article
- Prospects for Genomic Selection in Cassava Breeding.The plant genome · 2017Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genomic prediction is meant for estimating the breeding value using molecular marker data which has turned out to be a powerful tool for efficient utilization of germplasm resources and rapid improvement of cultivars. Model-based techniques have been widely used for prediction of breeding values of genotypes from genomewide association studies. However, application of the random forest (RF), a model-free ensemble learning method, is not widely used for prediction. In this study, the optimum values of tuning parameters of RF have been identified and applied to predict the breeding value of genotypes based on genomewide single-nucleotide polymorphisms (SNPs), where the number of SNPs (P variables) is much higher than the number of genotypes (n observations) (P » n). Further, a comparison was made with the model-based genomic prediction methods, namely, least absolute shrinkage and selection operator (LASSO), ridge regression (RR) and elastic net (EN) under P » n. It was found that the correlations between the predicted and observed trait response were 0.591, 0.539, 0.431 and 0.587 for RF, LASSO, RR and EN, respectively, which implies superiority of the RF over the model-based techniques in genomic prediction. Hence, we suggest that the RF methodology can be used as an alternative to the model-based techniques for the prediction of breeding value at genome level with higher accuracy.
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Registered trials
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