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DeepCCR: large-scale genomics-based deep learning method for improving rice breeding

Xiaoding Ma, Hao Wang, Shengyang Wu, Bing Han, Di Cui, Jin Liu, Qiang Zhang, Xiuzhong Xia, Peng Song, Cuifeng Tang, Leiyue Geng, Yaolong Yang, Shen Yan, Kunneng Zhou, Longzhi Han. 

Plant Biotechnology Journal; 2024; IF 13.80


In this study, we constructed the first large-scale Chinese rice population data set for rice genomic selection. We also conducted a comprehensive multiyear, multisite phenotypic survey and developed a companion deep neural network model to predict phenotypes and the ecological regions adapted for planting, as well as an easy-to-use online web server. The data set and results presented in this study offer a framework for breeders to quickly and efficiently breed superior rice varieties to address global food security issues. Additionally, with the increased number of materials in the data set and more comprehensive collection of multi-omics data (Wu et al., 2024), the predictive performance of DeepCCR will be further improved to enhance crop improvement programmes.