通过深度学习加速促进者识别和设计
Xinglong Wang1, Kangjie Xu2, Zhongshi Huang3
1Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, 88 Keling Road, Suzhou 215004, China; Science Center for Future Foods, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China.
Trends in biotechnology
|June 5, 2025
概括
深度学习 (DL) 彻底改变了促进器工程,用于精确的基因控制. 本综述探讨了DL在识别,预测和设计DNA促进器中用于增强生物功能的应用.
科学领域:
- 分子生物学分子生物学
- 合成生物学 合成生物学
- 生物信息学是一种生物信息学.
背景情况:
- 促进者是调节基因转录的关键DNA序列,影响细胞生长和寿命.
- 工程促进剂为自然产品生物合成等应用提供了对基因表达的精确控制.
- 传统的推动者工程方法包括理性设计和定向进化.
研究的目的:
- 审查深度学习 (DL) 技术在推动者工程中的应用.
- 突出DL驱动的方法促进者识别,强度预测,和新的设计.
- 讨论数据质量,特征提取和模型架构对DL模型性能的影响.
主要方法:
- 评论最近的文学深度学习应用程序在推动者工程.
- 对DL技术的分析,包括促进者设计的生成模型.
- 讨论影响预测准确性的因素,如数据库质量和功能工程.
主要成果:
- 深度学习模型显示了准确的促进者识别和强度预测的巨大潜力.
- 生成型DL模型使得能够重新设计具有所需特征的新型促销器.
- 数据库质量,特征提取方法和模型架构对DL模型性能产生了重大影响.
结论:
- 深度学习正在改变促进器工程,为生物控制提供了强大的工具.
- 进一步开发强大的DL模型需要注意数据质量和方法选择.
- 未来的前景包括推进DL,以实现更复杂,更可靠的推动者设计和工程.
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