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相关概念视频

MicroRNAs01:22

MicroRNAs

3.0K
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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lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

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In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
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mTOR Signaling and Cancer Progression03:03

mTOR Signaling and Cancer Progression

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The mammalian target of rapamycin or mTOR protein was discovered in 1994 due to its direct interaction with rapamycin. The protein gets its name from a yeast homolog called TOR. The mTOR protein complex in mammalian cells plays a major role in balancing anabolic processes such as the synthesis of proteins, lipids, and nucleotides and catabolic processes, such as autophagy in response to environmental cues, such as availability of nutrients and growth factors.
The mTOR pathway or the...
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相关实验视频

Updated: May 30, 2025

An In Vitro Protocol for Evaluating MicroRNA Levels, Functions, and Associated Target Genes in Tumor Cells
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An In Vitro Protocol for Evaluating MicroRNA Levels, Functions, and Associated Target Genes in Tumor Cells

Published on: May 21, 2019

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使用泛癌相关性模式预测microRNA点基因.

Shuting Lin1, Peng Qiu2

  • 1School of Biological Sciences, Georgia Institute of Technology, Atlanta, 30332, Georgia, USA.

BMC genomics
|January 28, 2025
PubMed
概括

这项研究引入了一种机器学习框架,用于预测新的微RNA (miRNA) - 基因相互作用. 该方法确定了以前未报告的调节关系,扩大了我们对基因表达控制的理解.

科学领域:

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

背景情况:

  • 微RNAs (miRNAs) 是基因表达的关键调节者.
  • 现有的miRNA目标基因数据库是有价值的,但不完整的,仅代表已知的相互作用的一小部分.
  • 发现新的miRNA-基因相互作用对于全面了解基因调节至关重要.

研究的目的:

  • 开发和应用机器学习模型来预测以前未报告的miRNA-目标基因相互作用.
  • 建立一个新的框架来识别显著的miRNA-基因对,使用相关性分析和机器学习.
  • 为未来的实验验证提供潜在miRNA-基因相互作用的资源.

主要方法:

  • 利用来自多种癌症类型的癌症基因组图谱 (TCGA) 的miRNA和基因表达数据.
  • 在所有miRNA-基因对之间进行了相关性分析,以生成特征.
  • 在精心策划的miRNA目标数据库上训练有素的机器学习模型,以预测新型的相互作用,确定一致预测的对是重要的.

主要成果:

  • 成功预测了许多新的miRNA-基因相互作用,其中5.5%与现有数据库和文献进行了验证.
  • 确定了具有高可靠性的显著miRNA-基因对,作为进一步研究的假设.
关键词:
基因基因 基因基因 基因基因机器学习是机器学习.在TCGA中,TCGA就是TCGA.这是一个小RNARNA.

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相关实验视频

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  • 在miRNA扰动数据集中预测的相关性方向和调节模式之间的观察一致性.
  • 结论:

    • 开发的机器学习框架提供了一种新的方法来发现以前未知的miRNA-基因关系.
    • 这项研究通过扩大已知的相互作用格局,显著提高了对miRNA介导的基因调节的理解.
    • 预测的相互作用为指导未来对miRNA功能的实验研究提供了宝贵的资源.