外文摘要：In the field of plant breeding, various machine learning models have been developed and studied to evaluate the genomic prediction (GP) accuracy of unseen phenotypes. Deep learning has shown promise. However, most studies on deep learning in plant breeding have been limited to small datasets, and only a few have explored its application in moderate-sized datasets. In this study, we aimed to address this limitation by utilizing a moderately large dataset. We examined the performance of a deep learning (DL) model and compared it with the widely used and powerful best linear unbiased prediction (GBLUP) model. The goal was to assess the GP accuracy in the context of a five-fold cross-validation strategy and when predicting complete environments using the DL model. The results revealed the DL model outperformed the GBLUP model in terms of GP accuracy for two out of the five included traits in the five-fold cross-validation strategy, with similar results in the other traits. This indicates the superiority of the DL model in predicting these specific traits. Furthermore, when predicting complete environments using the leave-one-environment-out (LOEO) approach, the DL model demonstrated competitive performance. It is worth noting that the DL model employed in this study extends a previously proposed multi-modal DL model, which had been primarily applied to image data but with small datasets. By utilizing a moderately large dataset, we were able to evaluate the performance and potential of the DL model in a context with more information and challenging scenario in plant breeding.
外文关键词：genomic prediction;machine learning methods;GBLUP model;multi-modal deep learning model;relationship matrices
作者：Perez-Rodriguez, Paulino;Crossa, Jose;Li, Huihui;Lillemo, Morten;Montesinos-Lopez, Osval A;Montesinos-Lopez, Abelardo;Crespo-Herrera, Leonardo;Dreisigacker, Susanna;Gerard, Guillermo;Vitale, Paolo;Saint Pierre, Carolina;Govindan, Velu;Tarekegn, Zerihun Tadesse;Flores, Moises Chavira;Ramos-Pulido, Sofia
作者单位：Univ Nacl Autonoma Mexico;Int Maize & Wheat Improvement Ctr CIMMYT;Norwegian Univ Life Sci NMBU;Univ Colima;Ctr Univ Ciencias Exactas Ingn CUCEI;Estadisticay Computo Aplicado Colegio Postgrad;Inst Crop Sci & CIMMYT China Off
期刊名称：FRONTIERS IN PLANT SCIENCE
期刊影响因子：0.0
出版年份：2024
出版刊次：15
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台