Multi-task genomic prediction using gated residual variable selection neural networks.
Journal:
BMC bioinformatics
Published Date:
Jul 7, 2025
Abstract
BACKGROUND: The recent development of high-throughput sequencing techniques provide massive data that can be used in genome-wide prediction (GWP). Although GWP is effective on its own, the incorporation of traditional polygenic pedigree information into GWP has been shown to further improve prediction accuracy. However, most of the methods developed in this field require that individuals with genomic information can be connected to the polygenic pedigree within a standard linear mixed model framework that involves calculation of computationally demanding matrix inverses of the combined pedigrees. The extension of this integrated approach to more flexible machine learning methods has been slow.
