Yan Jun, Wang Xiangfeng
National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100094, China; Frontiers Science Center for Molecular Design Breeding, China Agricultural University, Beijing 100094, China.
National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100094, China; Frontiers Science Center for Molecular Design Breeding, China Agricultural University, Beijing 100094, China.
Trends Plant Sci. 2023 Feb;28(2):199-210. doi: 10.1016/j.tplants.2022.08.018. Epub 2022 Sep 21.
Some of the biological knowledge obtained from fundamental research will be implemented in applied plant breeding. To bridge basic research and breeding practice, machine learning (ML) holds great promise to translate biological knowledge and omics data into precision-designed plant breeding. Here, we review ML for multi-omics analysis in plants, including data dimensionality reduction, inference of gene-regulation networks, and gene discovery and prioritization. These applications will facilitate understanding trait regulation mechanisms and identifying target genes potentially applicable to knowledge-driven molecular design breeding. We also highlight applications of deep learning in plant phenomics and ML in genomic selection-assisted breeding, such as various ML algorithms that model the correlations among genotypes (genes), phenotypes (traits), and environments, to ultimately achieve data-driven genomic design breeding.
从基础研究中获得的一些生物学知识将应用于植物育种实践。为了弥合基础研究与育种实践之间的差距，机器学习（ML）有望将生物学知识和组学数据转化为精准设计的植物育种。在此，我们综述了机器学习在植物多组学分析中的应用，包括数据降维、基因调控网络推断以及基因发现与优先级排序。这些应用将有助于理解性状调控机制，并识别可能适用于知识驱动型分子设计育种的目标基因。我们还重点介绍了深度学习在植物表型组学中的应用以及机器学习在基因组选择辅助育种中的应用，例如各种对基因型（基因）、表型（性状）和环境之间的相关性进行建模的机器学习算法，以最终实现数据驱动的基因组设计育种。
