Biological Prior Knowledge-Embedded Deep Neural Network for Plant Genomic Prediction.

ABSTRACT: Background/Objectives: Genomic prediction is a powerful approach that predicts phenotypic traits from genotypic information, enabling the acceleration of trait improvement in plant breeding. Traditional genomic prediction methods have primarily relied on linear mixed models, such as Genomic Best Linear Unbiased Prediction (GBLUP), and conventional machine learning methods like Support Vector Regression (SVR). Traditional methods are limited in handling high-dimensional data and nonlinear relationships. Thus, deep learning methods have also been applied to genomic prediction in recent years. Methods: We proposed iADEP, Integrated Additive, Dominant, and Epistatic Prediction model based on deep learning. Specifically, single nucleotide polymorphism (SNP) data integrating latent

SUBMITTER: Ye C 

PROVIDER: S-EPMC12027452 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature