MeNet:一种混合效应的深度神经网络,用于农业特征的多环境基因组预测
Yanhui Li1, Shengjie Ren1, Jixiang Li1
1State Key Laboratory for Crop Genetics and Germplasm Enhancement and Utilization, Jiangsu Nanjing National Field Scientific Observation and Research Station for Rice Germplasm, Key Laboratory of Biology, Genetics and Breeding of Japonica Rice in Mid-lower Yangtze River, Ministry of Agriculture and Rural Affairs, Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing 210095, China.
Plant communications
|November 21, 2025
概括
一个新的混合效应深度神经网络 (MeNet) 改善了作物特征的基因组预测. 这种先进的深度学习模型提高了预测的准确性和效率,有助于开发适应气候的作物.
科学领域:
- 基因组学就是基因组学.
- 植物育种 植物育种
- 人工智能的人工智能
背景情况:
- 基因组预测对农业至关重要,但深度学习方法表现不一致,缺乏可解释性.
- 现有的模型与复杂的遗传架构和环境相互作用作斗争.
研究的目的:
- 引入一个新的框架,MeNet,将统计学严谨性与深度学习相结合,用于先进的基因组预测.
- 提高预测农学特征的准确性和可解释性.
主要方法:
- 开发了一种混合效果的深度神经网络 (MeNet),用于随机和固定效果的双嵌入.
- 动态模拟表型特定的遗传关系和非线性变异效应.
- 在多个环境中对大米,小麦和玉米数据集进行了验证MeNet.
主要成果:
- 在36项评估中的34项中,MeNet在不同作物和特征的36项评估中超过了11个最先进的模型.
- 在90%较少的训练数据下实现了优越的跨环境预测,显示了57.07%的性能提升.
- 超出了理论上的遗传性极限,捕捉了表皮和基因与环境之间的相互作用.
结论:
- MeNet为基因组预测提供了强大的和可泛化的方法,超越了传统和深度学习模型.
- 该框架通过尽量减少现场数据要求,促进了气候适应性作物的高效育种.
- MeNet展示了基础模型在农业研发中的潜力.
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