EBMGP: a deep learning model for genomic prediction based on Elastic Net feature selection and bidirectional encoder representations from transformer's embedding and multi-head attention pooling.

ABSTRACT: Enhancing early selection through genomic estimated breeding values is pivotal for reducing generation intervals and accelerating breeding programs. Recently, deep learning (DL) approaches have gained prominence in genomic prediction (GP). Here, we introduce a novel DL framework for GP based on Elastic Net feature selection and bidirectional encoder representations from transformer's embedding and multi-head attention pooling (EBMGP). EBMGP applies Elastic Net for the selection of features, thereby diminishing the computational burden and bolstering the predictive accuracy. In EBMGP, SNPs are treated as "words," and groups of adjacent SNPs with similar LD levels are considered "sentences." By applying bidirectional encoder representations from transformers embeddings, this method models SN

SUBMITTER: Ji L 

PROVIDER: S-EPMC12009238 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature