TrG2P: A transfer-learning-based tool integrating multi-trait data for accurate prediction of crop yield.

ABSTRACT: Yield prediction is the primary goal of genomic selection (GS)-assisted crop breeding. Because yield is a complex quantitative trait, making predictions from genotypic data is challenging. Transfer learning can produce an effective model for a target task by leveraging knowledge from a different, but related, source domain and is considered a great potential method for improving yield prediction by integrating multi-trait data. However, it has not previously been applied to genotype-to-phenotype prediction owing to the lack of an efficient implementation framework. We therefore developed TrG2P, a transfer-learning-based framework. TrG2P first employs convolutional neural networks (CNN) to train models using non-yield-trait phenotypic and genotypic data, thus obtaining pre-trained models. S

SUBMITTER: Li J 

PROVIDER: S-EPMC11287160 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature