物候学
生物
植物育种
选择(遗传算法)
计算生物学
特质
生物技术
分子育种
基因组学
数据科学
推论
机器学习
人工智能
基因
计算机科学
基因组
遗传学
农学
程序设计语言
作者
Jun Yan,Xiangfeng Wang
标识
DOI:10.1016/j.tplants.2022.08.018
摘要
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.
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