Given the reliance on target and attribute databases for predictive performance and computational burden, it is imperative to invest in improving the computational efficiency of machine learning algorithms and computing resources.
This will help improve the accuracy and efficiency of genomic selection.
By enabling a faster
Machine learning seems to have a bright future in genomic selection. Machine learning techniques have the potentia how to buy phone numbers in bulkl to revolutionize genetic prediction as technology advances and computer resources become more widespread.
the processing of high-dimensional genomic data, the discovery of complex patterns, and an increase in the accuracy of predictions.
and more accurate selection of individuals with desired traits, the combination of machine learning algorithms with genomic selection enables improved breeding programs.
These methods allow
More research is needed to develop these methods, address computational issues, and explore their use for different plant and animal species.
We expect machine BRB Directory learning to become increasingly important in genomic selection as technology develops, accelerates the rate of genetic progress and supports the agricultural sector.