Obscured-ensemble models for genomic prediction.

ABSTRACT: Genomic Prediction (GP) uses dense whole-genome marker sets from lines of a crop to predict agronomic traits for untested genotypes. In recent years, deep learning (DL) approaches for genomic prediction have demonstrated state-of-the-art results. However, substantial variation exists in DL outcomes for GP as the success of DL is dependent on the architecture of the model used, as well as the amount of data available and the population structure of the individuals in the training set. In this paper, we consider an obscured model for GP, where the model is not provided with genomic content. The obscured model was intended to evaluate the possibility of so-called shortcut learning in GP.We conclude that we can perform GP using the obscured model with only 20% of the obscured markers from each

SUBMITTER: Saha R 

PROVIDER: S-EPMC12617858 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature