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相关概念视频

siRNA - Small Interfering RNAs02:30

siRNA - Small Interfering RNAs

16.8K
Small interfering RNAs, or siRNAs, are short regulatory RNA molecules that can silence genes post-transcriptionally, as well as the transcriptional level in some cases. siRNAs are important for protecting cells against viral infections and silencing transposable genetic elements.
In the cytoplasm, siRNA is processed from a double-stranded RNA, which comes from either endogenous DNA transcription or exogenous sources like a virus. This double-stranded RNA is then cleaved by the...
16.8K

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相关实验视频

Updated: Jul 1, 2025

Predicting Gene Silencing Through the Spatiotemporal Control of siRNA Release from Photo-responsive Polymeric Nanocarriers
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预测基于多视图学习策略的化学修饰siRNA效率.

Tianyuan Liu1, Junyang Huang2, Delun Luo3

  • 1Innovative Institute of Chinese Medicine and Pharmacy, Academy for Interdiscipline, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.

International journal of biological macromolecules
|March 9, 2024
PubMed
概括

我们开发了Cm-siRPred,这是一个机器学习工具,用于预测化学修饰siRNA的效率. 这种算法有助于通过改进化学修饰和预测它们的有效性来设计更好的siRNA药物.

关键词:
化学修饰是一种化学修饰.多视图学习多视图学习siRNA 是一个RNA.

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科学领域:

  • 生物技术是生物技术.
  • 计算化学计算化学
  • 药物发现 药物发现 药物发现

背景情况:

  • 合理修改小干扰RNA (siRNA) 对于开发有效的siRNA疗法至关重要.
  • 机器学习 (ML) 可以通过预测效率,减少开发时间和成本来优化化学修饰siRNA (cm-siRNA) 的设计.
  • 目前用于cm-siRNA效率预测的in-silico方法面临着有限的数据集,数据表现差以及缺乏可解释性的挑战.

研究的目的:

  • 开发一种可靠和可解释的ML算法,用于预测化学修饰siRNA的效率.
  • 解决现有的in-silico方法在数据表示和可解释性方面的局限性.
  • 通过改进化学修饰设计,为加速siRNA药物开发提供实用工具.

主要方法:

  • 开发了使用多视图学习策略的Cm-siRPred算法.
  • 使用双链序列,化学修饰和物理化学性质来表示cm-siRNA.
  • 集成了一个交叉注意力模型用于全球相关性和一个双层CNN用于本地特征学习.

主要成果:

  • 在交叉验证和独立数据集上,cm-siRPred表现出了卓越的性能.
  • 该算法通过对已批准的siRNA药物的案例研究显示出强度和概括能力.
  • 为高效的cm-siRNA效率预测和设计辅助开发了一个用户友好的Web服务器.

结论:

  • Cm-siRPred是一个实用的工具,为siRNA化学修饰和药物效率研究提供了宝贵的技术支持.
  • 该算法有效地协助开发新的小核酸药物.
  • Cm-siRPred是免费可用的,促进siRNA治疗研究和开发的进步.