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相关概念视频

End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting the...

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Characterization of Functionally Associated miRNAs in Glioblastoma and their Engineering into Artificial Clusters for Gene Therapy
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DeepMEns:基于多个特征预测sgRNA目标活动的集合模型.

Shumei Ding1, Jia Zheng1, Cangzhi Jia1

  • 1School of Science, Dalian Maritime University, Dalian 116026, China.

Briefings in functional genomics
|November 11, 2024
PubMed
概括
此摘要是机器生成的。

DeepMEns是一个可解释的深度学习模型,通过准确预测单导向RNA (sgRNA) 目标活动来增强CRISPR/Cas9基因编辑. 这一进步提高了基因编辑应用的可靠性和效率.

关键词:
在美国,CNN是CNN.这就是CRISPR/Cas9的作用.注意力机制注意力机制集成算法集成算法集成算法位置编码位置编码.sgRNARNA 是一个

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科学领域:

  • 基因组学和生物信息学
  • 分子生物学分子生物学
  • 计算生物学 计算生物学

背景情况:

  • CRISPR/Cas9系统,特别是Streptococcus pyogenes Cas9 (SpCas9),对基因编辑有很大的前景.
  • 单导向RNA (sgRNA) 目标效率的变化限制了CRISPR/Cas9的成功应用.
  • 预测sgRNA活动的现有深度学习模型缺乏解释性,并有能力提高性能.

研究的目的:

  • 开发一个可解释的深度学习模型,DeepMEns,用于预测sgRNA在目标上的活动.
  • 在解释性和预测准确性方面克服现有模型的局限性.
  • 通过改进的sgRNA设计,提高CRISPR/Cas9基因编辑的效率和可靠性.

主要方法:

  • 开发了DeepMEns,这是一个使用深度学习的集体可解释模型.
  • 集成了三种不同的输入特征类型:二次结构的一次性编码,DNA形状特征和位置编码特征.
  • 使用转换器编码器的卷积神经网络 (CNN) 和具有注意力机制的长短期记忆 (LSTM) 网络.
  • 构建了五个子回归器,对最终输出的预测进行了平均.

主要成果:

  • DeepMEns实现了最先进的性能,证明了卓越的预测准确性.
  • 与之前的预测器相比,该模型在10个独立测试数据集中的6个中获得了最高的斯皮尔曼相关系数.
  • 废弃分析证实,整体策略显著提高了预测模型的性能.

结论:

  • DeepMEns在预测sgRNA目标活动方面取得了重大进展,改善了CRISPR/Cas9基因编辑.
  • 该模型的可解释性允许更好地了解影响sgRNA效率的因素.
  • DeepMEns为设计更有效的sgRNA提供了可靠的工具,为更精确的基因编辑结果铺平了道路.