A review of multimodal deep learning methods for genomic-enabled prediction in plant breeding.

ABSTRACT: Deep learning methods have been applied when working to enhance the prediction accuracy of traditional statistical methods in the field of plant breeding. Although deep learning seems to be a promising approach for genomic prediction, it has proven to have some limitations, since its conventional methods fail to leverage all available information. Multimodal deep learning methods aim to improve the predictive power of their unimodal counterparts by introducing several modalities (sources) of input information. In this review, we introduce some theoretical basic concepts of multimodal deep learning and provide a list of the most widely used neural network architectures in deep learning, as well as the available strategies to fuse data from different modalities. We mention some of the availa

SUBMITTER: Montesinos-Lopez OA 

PROVIDER: S-EPMC11631469 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature