Machine learning bridges omics sciences and plant breeding

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 priori-tization. These applications will facilitate understanding trait regulation mecha-nisms 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.

Automated summary

Machine learning (ML) shows great potential in translating biological knowledge and omics data into precision-designed plant breeding, bridging the gap between basic research and breeding practice. ML algorithms can be used to analyze multi-omics data, infer gene-regulation networks, and prioritize target genes for knowledge-driven molecular design breeding.