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Improving Genomic Prediction with Machine Learning Incorporating TPE for Hyperparameters Optimization.

Mang Liang¹, Bingxing An¹, Keanning Li¹

¹Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China.

|November 24, 2022

Summary

This summary is machine-generated.

We developed an automatic hyperparameter tuning method using the Tree-structured Parzen Estimator (TPE) to simplify machine learning for genomic prediction. This approach enhances prediction accuracy in animal and plant breeding programs.

Keywords:

genomic prediction hyperparameters optimization machine learning tree-structured Parzen estimator

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Area of Science:

Genomics
Machine Learning
Quantitative Genetics

Background:

Machine learning (ML) excels at analyzing high-throughput sequencing genome data.
Hyperparameter tuning in ML is complex, limiting its use in breeding programs.

Purpose of the Study:

To integrate the Tree-structured Parzen Estimator (TPE) for automatic hyperparameter optimization in ML-based genomic prediction (GP).
To simplify and enhance the application of ML in animal and plant breeding.

Main Methods:

Applied TPE to optimize hyperparameters for Kernel Ridge Regression (KRR) and Support Vector Regression (SVR).
Compared prediction accuracy of TPE-optimized models (KRR-TPE, SVR-TPE) against GBLUP, random search (RS), and grid search (Grid) using simulation and real datasets.

Main Results:

KRR-TPE demonstrated superior prediction ability and convenience across all tested populations.
KRR-TPE achieved significant average accuracy improvements of 8.73% in Chinese Simmental beef cattle and 6.08% in Loblolly pine compared to GBLUP.

Conclusions:

TPE-based hyperparameter optimization effectively enhances ML model performance for genomic prediction.
This integrated approach is poised to accelerate breeding progress by facilitating ML adoption.