GE-BiFormer: bidirectional cross-attention integration of genomic and Enviromic data for genotype-by-environment prediction in maize.

Abstract excerpt

Phenotypic variation is shaped by genotype, environment, and their interactions. Accurately predicting crop performance across diverse environments therefore requires models capable of capturing these complex and context-dependent relationships. Here, we developed GE-BiFormer, an explainable multimodal deep learning framework for genotype-by-environment prediction. GE-BiFormer integrates genomic and enviromic...

Topics

Share this publication in a Topic to start or enrich a Post.

• Share to Deep Learning
• Share to Gene-Environment Interaction