PDF(Pubmed)
Abstract：
Genome editing is a promising technique that has been broadly utilized for basic gene function studies and trait improvements. Simultaneously, the exponential growth of computational power and big data now promote the application of machine learning for biological research. In this regard, machine learning shows great potential in the refinement of genome editing systems and crop improvement. Here, we review the advances of machine learning to genome editing optimization, with emphasis placed on editing efficiency and specificity enhancement. Additionally, we demonstrate how machine learning bridges genome editing and crop breeding, by accurate key site detection and guide RNA design. Finally, we discuss the current challenges and prospects of these two techniques in crop improvement. By integrating advanced genome editing techniques with machine learning, progress in crop breeding will be further accelerated in the future.
摘要：
基因组编辑是一种有前途的技术，已广泛用于基本基因功能研究和性状改善。同时,计算能力和大数据的指数增长促进了机器学习在生物学研究中的应用。在这方面,机器学习在基因组编辑系统的完善和作物改良方面显示出巨大的潜力。这里,我们回顾了机器学习在基因组编辑优化方面的进展，重点放在编辑效率和特异性增强上。此外,我们展示了机器学习如何连接基因组编辑和作物育种，通过准确的关键位点检测和指导RNA设计。最后,我们讨论了这两种技术在作物改良中的当前挑战和前景。通过将先进的基因组编辑技术与机器学习相结合，未来作物育种的进展将进一步加快。
