Deep learning for genomic selection of aquatic animals.

ABSTRACT: Genomic selection (GS) applied to the breeding of aquatic animals has been of great interest in recent years due to its higher accuracy and faster genetic progress than pedigree-based methods. The genetic analysis of complex traits in GS does not escape the current excitement around artificial intelligence, including a renewed interest in deep learning (DL), such as deep neural networks (DNNs), convolutional neural networks (CNNs), and autoencoders. This article reviews the current status and potential of DL applications in phenotyping, genotyping and genomic estimated breeding value (GEBV) prediction of GS. It can be seen from this article that CNNs obtain phenotype data of aquatic animals efficiently, and without injury; DNNs as single nucleotide polymorphism (SNP) variant callers are cr

SUBMITTER: Wang Y 

PROVIDER: S-EPMC11602929 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature