数字农科院2.0

MtCro: multi-task deep learning framework improves multi-trait genomic prediction of crops

文献类型： 外文期刊

作者： Chao, Dian;Wang, Hao;Wan, Fengqiang;Yan, Shen;Fang, Wei;Yang, Yang

作者机构：

关键词： Deep learning;Genomic prediction;Multi-task learning;Crop breeding

期刊名称： PLANT METHODS

ISSN：

年卷期： 2025 年 21 卷 1 期

页码：

收录情况： SCIE（2025版）

摘要： Genomic Selection (GS) predicts traits using genome-wide markers, speeding up genetic progress and enhancing breeding efficiency. Recent emphasis has been placed on deep learning models to enhance prediction accuracy. However, current deep learning models focus on learning specific phenotypes for the given task, overlooking the inter-correlations among different phenotypes. In response, we introduce MtCro, a multi-task learning approach that simultaneously captures diverse plant phenotypes within a shared parameter space. Extensive experiments reveal that MtCro outperforms mainstream models, including DNNGP and SoyDNGP, with performance gains of 1-9% on the Wheat2000 dataset, 1-8% on Wheat599, and 1-3% on Maize8652. Furthermore, comparative analysis shows a consistent 2-3% improvement in multi-phenotype predictions, emphasizing the impact of inter-phenotype correlations on accuracy. By leveraging multi-task learning, MtCro efficiently captures diverse plant phenotypes, enhancing both model training efficiency and prediction accuracy, ultimately accelerating the progress of plant genetic breeding. Our code is available on https://github.com/chaodian12/mtcro.

分类号：

  • 相关文献

[1]A comparative study highlights superiority of LSTM in crop genomic prediction. Ruiqing Pan,Yaolong Yang,Yuanyuan Zhang,Qun Xu,Yue Feng,Junyu Chen,Wei Li,Shoupu He,Xinghua Wei,Mengchen Zhang. 2025

[2]A novel method for maize leaf disease classification using the RGB-D post-segmentation image data. Nan, Fei,Song, Yang,Yu, Xun,Nie, Chenwei,Liu, Yadong,Bai, Yali,Zou, Dongxiao,Wang, Chao,Yin, Dameng,Yang, Wude,Jin, Xiuliang. 2023

[3]DeepSATA: A Deep Learning-Based Sequence Analyzer Incorporating the Transcription Factor Binding Affinity to Dissect the Effects of Non-Coding Genetic Variants. Ma, Wenlong,Fu, Yang,Bao, Yongzhou,Wang, Zhen,Lei, Bowen,Zheng, Weigang,Wang, Chao,Liu, Yuwen. 2023

[4]Fast-forwarding plant breeding with deep learning-based genomic prediction. Gao, Shang,Yu, Tingxi,Rasheed, Awais,Wang, Jiankang,Crossa, Jose,Hearne, Sarah,Li, Huihui. 2025

[5]Editorial: Insights in functional and applied plant genomics: 2023. Huihui Li. 2025

[6]EMSAM: enhanced multi-scale segment anything model for leaf disease segmentation. Junlong Li,Quan Feng,Jianhua Zhang,Sen Yang. 2025

[7]Soybean yield estimation and lodging discrimination based on lightweight UAV and point cloud deep learning. Longyu Zhou,Dezhi Han,Guangyao Sun,Yaling Liu,Xiaofei Yan,Hongchang Jia,Long Yan,Puyu Feng,Yinghui Li,Lijuan Qiu,Yuntao Ma. 2025

[8]A novel cross-modal decoupling dual-spectral fusion system and method: A case study on the on-site estimation of fresh tobacco leaf curing characteristics. Zhongtao Huang,Shichang Wang,Rongguang Zhu,Yapeng Kang,Lingfeng Meng,Jie Ren. 2025

[9]Genetic gains in grain yield, net photosynthesis and stomatal conductance achieved in Henan Province of China between 1981 and 2008. Xia, X. C.,He, Z. H.,Zheng, T. C.,Yin, G. H.,Wang, L. N.,Han, Y. L.,Huang, F.,Tang, J. W.,Zhang, X. K.,Chen, L.,He, Z. H..

[10]Genetic diversity analysis of abiotic stress response gene TaSnRK2.7-A in common wheat. Zhang, Hongying,Mao, Xinguo,Zhang, Jianan,Chang, Xiaoping,Jing, Ruilian,Zhang, Hongying,Wang, Chengshe.

[11]Genetic Diversity of Common Carpetgrass Revealed by Amplified Fragment Length Polymorphism Markers. Wang, Zan,Kenworthy, Kevin E.,Wang, Zan,Wu, Yanqi,Wang, Zan.

[12]Using Interactome Big Data to Crack Genetic Mysteries and Enhance Future Crop Breeding. Leiming Wu,Linqian Han,Qing Li,Guoying Wang,Hongwei Zhang,Lin Li. 2021

[13]The Research Of Crop Breeding T.echnical Architecture Based On C yber-Physical System(Cps). Yue, E, Zhu, YP. 2017

[14]Breeding Bread-Making Wheat Varieties for Organic Farming Systems: The Need to Target Productivity, Robustness, Resource Use Efficiency and Grain Quality Traits. Rempelos, Leonidas,Wang, Juan,Sufar, Enas Khalid,Almuayrifi, Mohammed Saleh Bady,Knutt, Daryl,Leifert, Halima,Leifert, Alice,Wilkinson, Andrew,Shotton, Peter,Hasanaliyeva, Gultekin,Bilsborrow, Paul,Wilcockson, Steve,Volakakis, Nikolaos,Markellou, Emilia,Zhao, Bingqiang,Jones, Stephen,Iversen, Per Ole,Leifert, Carlo. 2023

[15]The Promising Nanovectors for Gene Delivery in Plant Genome Engineering. Zhi, Heng,Zhou, Shengen,Pan, Wenbo,Shang, Yun,Zeng, Zhanghua,Zhang, Huawei. 2022

[16]Editorial: Multi-omics strategies to analyze complex agronomic traits in plants. Lin Chen,Guo Fei Tan. 2023

[17]Innovative opportunities for gene editing technology in crop breeding: from the perspective of literature analysis. Hui Zhang,Ru Yao,Qian Jia,Shijie Qi,Ying He,Jingjuan Zhao,Limin Chuan. 2025

[18]Genetic Regulation and Breeding Application of Medium-Chain Fatty Acids Metabolism in Rice. Muhammad Zubair,Xiaohong Tong,Aneela Ashraf,Hongzhou Li,Guanghao Li,Ai Xin,Jiale Chen,Yifeng Wang,Zhiyong Li,Jie Huang,Yu Cheng,Jian Zhang,Jiezheng Ying. 2025

[19]Accuracy of genomic prediction using low-density marker panels. Zhang, Z.,Ding, X.,Liu, J.,Zhang, Q.,Zhang, Z.,de Koning, D. -J.,Zhang, Z.,de Koning, D. -J.,de Koning, D. -J..

[20]Canine hip dysplasia is predictable by genotyping. Zhang, Z.,Guo, G.,Wang, Y.,Zhang, Y.,Guo, G.,Zhou, Z.,Li, J.,Zhou, Z.,Hunter, L.,Friedenberg, S.,Krotscheck, U.,Todhunter, R.,Zhu, L.,Lust, G.,Harris, S.,Jones, P.,Sandler, J.,Zhao, K.,Zhou, Z.. 2011

作者其他论文 更多>>