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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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siRNA - Small Interfering RNAs02:30

siRNA - Small Interfering RNAs

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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...
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相关实验视频

Updated: Jun 9, 2025

Genome-wide Screen for miRNA Targets Using the MISSION Target ID Library
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Genome-wide Screen for miRNA Targets Using the MISSION Target ID Library

Published on: April 6, 2012

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sChemNET:一种深度学习框架,用于预测准微RNA功能的小分子.

Diego Galeano1,2, Imrat3, Jeffrey Haltom4,5

  • 1Department of Electronics and Mechatronics Engineering, Facultad de Ingeniería, Universidad Nacional de Asunción - FIUNA, Luque, Paraguay. dgaleano@ing.una.py.

Nature communications
|October 23, 2024
PubMed
概括
此摘要是机器生成的。

我们开发了sChemNET,这是一个深度学习框架,用于预测影响微RNA (miRNA) 活动的小分子. 该工具有助于识别与miRNA失调相关的疾病的潜在治疗方法.

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科学领域:

  • 生物化学 生物化学
  • 计算生物学 计算生物学
  • 遗传学 是一个遗传学.

背景情况:

  • 微RNAs (miRNAs) 是关键的调节者,涉及到各种人类疾病,包括癌症和传染病.
  • 用小分子调节miRNA活动或它们的基因,为疾病治疗提供了治疗途径.
  • 由于有限的数据集,预测小分子-miRNA相互作用具有挑战性.

研究的目的:

  • 开发一个通用的深度学习框架,sChemNET,用于预测影响miRNA生物活性的小分子.
  • 通过使模型能够从广泛的化学结构信息中学习来克服数据稀疏性.
  • 在生物系统中实验验证框架的预测.

主要方法:

  • 开发了sChemNET,这是一个使用化学结构和序列信息的深度学习框架.
  • 采用一个客观函数,从大型,未标记的化学数据集中学习化学空间.
  • 在斑马鱼和体外模型中进行了预测小分子针对miR-451和miR-181网络的实验验证.

主要成果:

  • sChemNET成功预测了影响miRNA生物活性的小分子.
  • 实验验证证证实了在生物测试中预测的小分子的有效性.
  • 证明了该框架能够识别用于miRNA向的生物活性小分子的能力.

结论:

  • 开发的sChemNET框架提供了一种强大的方法来识别调节miRNA活动的小分子.
  • 这种工具可以加速对miRNA相关疾病的新疗法的发现.
  • 机器学习框架显示出对人类和哺乳动物生物体应用的前景.