ArticleBiology2022
Improving Genomic Prediction with Machine Learning Incorporating TPE for Hyperparameters Optimization.
Article in Biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed, 22 citations in OpenAlex.
- From linear models to deep learning: statistical advances in genomic selection for animal breeding.Journal of animal science and biotechnology · 2026Review
- Synthetic data enables human-grade microtubule analysis with foundation models for segmentation.PLoS computational biology · 2026Article
- Impact of scale parameter for marker variance prior in some Bayesian whole-genome regression methods.Mammalian genome : official journal of the International Mammalian Genome Society · 2026Article
- Benchmarking hyperparameter optimization strategies for crop genomic prediction.Frontiers in genetics · 2026Article
- Multi-marker GWAS and variant-specific genomic prediction for growth traits in Pacific white shrimp.Scientific reports · 2025Article
- ExAutoGP: Enhancing Genomic Prediction Stability and Interpretability with Automated Machine Learning and SHAP.Animals : an open access journal from MDPI · 2025Article
- MSXFGP: combining improved sparrow search algorithm with XGBoost for enhanced genomic prediction.BMC bioinformatics · 2023Article
- Genetic Parameter and Hyper-Parameter Estimation Underlie Nitrogen Use Efficiency in Bread Wheat.International journal of molecular sciences · 2023Article
- Stacked ensembles on basis of parentage information can predict hybrid performance with an accuracy comparable to marker-based GBLUP.Frontiers in plant science · 2023Article
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Authors and funding
12 authors at 1 institution in 1 country.
Funding
Abstract
Depending on excellent prediction ability, machine learning has been considered the most powerful implement to analyze high-throughput sequencing genome data. However, the sophisticated process of tuning hyperparameters tremendously impedes the wider application of machine learning in animal and plant breeding programs. Therefore, we integrated an automatic tuning hyperparameters algorithm, tree-structured Parzen estimator (TPE), with machine learning to simplify the process of using machine learning for genomic prediction. In this study, we applied TPE to optimize the hyperparameters of Kernel ridge regression (KRR) and support vector regression (SVR). To evaluate the performance of TPE, we compared the prediction accuracy of KRR-TPE and SVR-TPE with the genomic best linear unbiased prediction (GBLUP) and KRR-RS, KRR-Grid, SVR-RS, and SVR-Grid, which tuned the hyperparameters of KRR and SVR by using random search (RS) and grid search (Gird) in a simulation dataset and the real datasets. The results indicated that KRR-TPE achieved the most powerful prediction ability considering all populations and was the most convenient. Especially for the Chinese Simmental beef cattle and Loblolly pine populations, the prediction accuracy of KRR-TPE had an 8.73% and 6.08% average improvement compared with GBLUP, respectively. Our study will greatly promote the application of machine learning in GP and further accelerate breeding progress.
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