Ensemble of Bayesian alphabets via constraint weight optimization strategy improves genomic prediction accuracy.

ABSTRACT: This study proposes a weight optimization-based ensemble framework aimed at improving genomic prediction accuracy. It incorporates 8 Bayesian models-BayesA, BayesB, BayesC, BayesBpi, BayesCpi, BayesR, BayesL, and BayesRR in the ensemble framework, where the weight assigned to each model was optimized using genetic algorithm method. The performance of the ensemble model, named EnBayes, was evaluated on 18 datasets from 4 crop species, showing improved prediction accuracy compared to individual Bayesian models. New objective functions were proposed to improve prediction accuracy in terms of both Pearson's correlation coefficient and mean square error. The accuracy of the ensemble model was found to be associated with the number of models considered in the framework, where a few more accurate

SUBMITTER: Meher PK 

PROVIDER: S-EPMC12405891 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature