通过深度学习推进CRISPR:对模型和数据库的全面审查
Roghayyeh Alipanahi1, Leila Safari1, Alireza Khanteymoori2
1Department of Computer Engineering, University of Zanjan, Zanjan, Iran.
Molecular therapy. Nucleic acids
|September 29, 2025
概括
预测CRISPR基因编辑效率对于治疗开发至关重要. 机器学习和深度学习工具,以及全面的CRISPR数据库,是提高预测准确度和最小化非目标效应的关键.
科学领域:
- 遗传学和基因组学 遗传学和基因组学
- 生物信息学是一种生物信息学.
- 生物技术是生物技术.
背景情况:
- 克里斯普尔技术提供了强大的向基因组编辑能力.
- 非目标效应是限制CRISPR基因编辑疗法的临床应用的主要障碍.
- 准确预测CRISPR编辑效率对于推进基因编辑疗法至关重要.
研究的目的:
- 审查可用的CRISPR数据库,以提高CRISPR在目标上和目标之外活动的预测.
- 突出机器学习 (ML) 和深度学习 (DL) 在提高CRISPR效率预测中的作用.
- 解决大型可搜索数据库的需求,以支持CRISPR研究中的ML/DL方法.
主要方法:
- 审查关于CRISPR数据库的现有文献.
- 分析机器学习和深度学习方法来预测CRISPR活动.
- 讨论序列特征及其纳入预测模型的讨论.
主要成果:
- 目前对CRISPR的目标和非目标活动的预测准确性受到训练数据可用性的限制.
- 机器学习和深度学习正在成为CRISPR效率预测的领先方法.
- 开发全面且可搜索的CRISPR数据库对于推进ML/DL预测工具至关重要.
结论:
- 提高CRISPR效率的预测对于基因编辑疗法的安全有效的临床应用至关重要.
- 通过广泛的数据库支持的机器学习和深度学习方法,在克服CRISPR的目标之外的挑战方面显示出重大前景.
- 进一步开发和将序列特征集成到DL模型中,预计将提高预测准确性和临床实用性.
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