GWKBR: a novel method integrating machine learning and Bayesian inference framework to improve genomic prediction accuracy.

Abstract excerpt

Non-additive genetic effects pose significant challenges to traditional genomic prediction methods. Inspired by the ability of kernel-based machine learning methods to capture non-additive effects and the accurate genomic prediction of Bayesian methods, we developed a novel genomic prediction method, Genome-Wide Association Studies-Weighted Gaussian Kernel Bayesian Regression (GWKBR), which introduces a new form...

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