Machine learning for genomic prediction of growth traits in aquaculture: a case study of the Australasian snapper (Chrysophrys auratus)

Integration of domain knowledge into machine learning frameworks effectively addresses missing values and high-dimensional challenges in snapper genomic data. The evaluated framework demonstrates that Chi2-DWD represents a promising combination for genomic prediction tasks. The DK Pre-filtering workflow as a pre-filtering method successfully removes redundant features without affecting model performance. Selected features showed biological significance and were confirmed to be associated with growth traits based on biological analysis, providing valuable insights for selective breeding programs.