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相关概念视频

MicroRNAs01:22

MicroRNAs

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MicroRNA (miRNA) are short, regulatory RNA transcribed from introns (non-coding regions of a gene) or intergenic regions (stretches of DNA present between genes). Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself, forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After the pre-miRNA...
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相关实验视频

Updated: Jul 17, 2025

In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions
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In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions

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一个高效的circRNA-miRNA相互作用预测模型,通过结合生物文本挖掘和基于波纹扩散的稀疏网络结构嵌入嵌入.

Xin-Fei Wang1, Chang-Qing Yu1, Zhu-Hong You2

  • 1School of Information Engineering, Xijing University, Xi'an, China.

Computers in biology and medicine
|September 6, 2023
PubMed
概括

生物DGW-CMI使用生物文本挖掘和网络嵌入预测circRNA-miRNA相互作用. 这种计算方法通过提高预测循环RNA-miRNA相互作用的准确性来增强疾病诊断和治疗.

关键词:
生物文本挖掘 生物文本挖掘生物标志物 生物标志物发现结构性角色的发现结构嵌入式嵌入式环RNA-miRNA相互作用

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科学领域:

  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学
  • 基因组学就是基因组学.

背景情况:

  • 循环RNAs (circRNAs) 在人类疾病的发展和进展中至关重要.
  • 计算方法可以加速发现与疾病相关的circRNAs.
  • 现有的网络嵌入模型与稀疏的生物网络作斗争.

研究的目的:

  • 开发一个准确的计算模型来预测circRNA-miRNA相互作用 (CMI).
  • 克服现有网络嵌入方法在稀疏生物网络中的局限性.

主要方法:

  • 生物DGW-CMI集成生物文本挖掘 (BERT) 与基于波纹扩散的稀疏网络结构嵌入.
  • 它构建了一个circRNA-miRNA相互作用网络并提取拓特征.
  • 否定自编码器和轻GBM用于功能集成和预测.

主要成果:

  • 生物DGW-CMI在三个CMI预测数据集中实现了卓越的性能.
  • 该模型成功预测了来自circ-ITCH数据库的所有8个测试的circRNA-miRNA相互作用.
  • 这表明在预测功能性circRNA-miRNA关系方面具有很高的准确性和可靠性.

结论:

  • 生物DGW-CMI提供了一个强大的计算方法来预测circRNA-miRNA相互作用.
  • 该模型在稀疏网络中的有效性促进了对疾病洞察力的circRNA研究.
  • 这种方法对新的诊断和治疗策略具有前景.