ArticleAnimals : an open access journal from MDPI2021
Prediction of Hanwoo Cattle Phenotypes from Genotypes Using Machine Learning Methods.
Article in Animals : an open access journal from MDPI, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed.
- From linear models to deep learning: statistical advances in genomic selection for animal breeding.Journal of animal science and biotechnology · 2026Review
- Impact of QTL number, heritability and reference population size on the benefit of machine learning models over GBLUP in genomic prediction.Genetics, selection, evolution : GSE · 2026Article
- Recent progress in genomic prediction for Hanwoo cattle and its implications for beef quality: review.Animal bioscience · 2026Article
- Feed-forward neural networks for genomic selection: insights into varying levels of dominance heritability.BMC genomics · 2026Article
- Haplotype applications in genomic selection.Genome biology · 2026Review
- Advancements in Animal Breeding: From Mendelian Genetics to Machine Learning.International journal of molecular sciences · 2025Review
- Enhancing Genomic Prediction Accuracy of Reproduction Traits in Rongchang Pigs Through Machine Learning.Animals : an open access journal from MDPI · 2025Article
- 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
- Benchmarking of feed-forward neural network models for genomic prediction of quantitative traits in pigs.Frontiers in genetics · 2025Article
- Machine Learning for the Genomic Prediction of Growth Traits in a Composite Beef Cattle Population.Animals : an open access journal from MDPI · 2024Article
- An overview of recent technological developments in bovine genomics.Veterinary and animal science · 2024Review
- Estimation of genetic correlations and genomic prediction accuracy for reproductive and carcass traits in Hanwoo cows.Journal of animal science and technology · 2024Article
- Article
- A review of machine learning models applied to genomic prediction in animal breeding.Frontiers in genetics · 2023Review
- An effective hyper-parameter can increase the prediction accuracy in a single-step genetic evaluation.Frontiers in genetics · 2023Article
- Prediction performance of linear models and gradient boosting machine on complex phenotypes in outbred mice.G3 (Bethesda, Md.) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Hanwoo was originally raised for draft purposes, but the increase in local demand for red meat turned that purpose into full-scale meat-type cattle rearing; it is now considered one of the most economically important species and a vital food source for Koreans. The application of genomic selection in Hanwoo breeding programs in recent years was expected to lead to higher genetic progress. However, better statistical methods that can improve the genomic prediction accuracy are required. Hence, this study aimed to compare the predictive performance of three machine learning methods, namely, random forest (RF), extreme gradient boosting method (XGB), and support vector machine (SVM), when predicting the carcass weight (CWT), marbling score (MS), backfat thickness (BFT) and eye muscle area (EMA). Phenotypic and genotypic data (53,866 SNPs) from 7324 commercial Hanwoo cattle that were slaughtered at the age of around 30 months were used. The results showed that the boosting method XGB showed the highest predictive correlation for CWT and MS, followed by GBLUP, SVM, and RF. Meanwhile, the best predictive correlation for BFT and EMA was delivered by GBLUP, followed by SVM, RF, and XGB. Although XGB presented the highest predictive correlations for some traits, we did not find an advantage of XGB or any machine learning methods over GBLUP according to the mean squared error of prediction. Thus, we still recommend the use of GBLUP in the prediction of genomic breeding values for carcass traits in Hanwoo cattle.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.