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相关概念视频

Regulated mRNA Transport02:22

Regulated mRNA Transport

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In eukaryotes, transcription and translation are compartmentalized; an mRNA is first synthesized in the nucleus and then selectively transported to the cytoplasm for protein synthesis. Before transport, a pre-mRNA undergoes several steps of post-transcriptional modifications including splicing, 5' capping, and the addition of a poly-adenine tail. Various proteins bind to the pre-mRNA during these modifications. The mRNA transport takes place with the help of multiple proteins playing...
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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

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Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
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MGFmiRNAloc:使用分子图形特征和卷积块注意模块预测miRNA亚细胞定位.

Ying Liang, Xiya You, Zequn Zhang

    IEEE/ACM transactions on computational biology and bioinformatics
    |April 1, 2024
    PubMed
    概括

    这项研究介绍了MGFmiRNAloc,一种使用图形卷积网络预测微RNA (miRNA) 亚细胞定位的新计算方法. 该方法准确地识别了miRNA位置,改进了现有的方法.

    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
    • 分子生物学分子生物学

    背景情况:

    • 微RNA (miRNAs) 在各种细胞功能中起着至关重要的作用.
    • 精确预测miRNA亚细胞定位是必不可少的,但在计算上具有挑战性.
    • 目前用于miRNA本地化预测的方法存在局限性,需要改进方法.

    研究的目的:

    • 开发一种新的计算方法,MGFmiRNAloc,用于预测微RNAs (miRNAs) 的亚细胞局部化.
    • 使用简化分子输入线输入系统 (SMILES) 格式和图形卷积网络 (GCN) 进行RNA序列特征提取.
    • 通过道注意力和空间注意力机制 (CBAM) 提高预测准确性.

    主要方法:

    • 使用SMILES格式表示MGFmiRNAloc.loc的RNA序列.
    • 使用GCN从RNA序列中提取原子节点和拓结构,创建分子地图特征.
    • 整合CBAM来完善特征提取,以改善信息挖掘.
    • 使用预测模块进行最终亚细胞局部化检测.

    主要成果:

    • 与现有的最先进的方法相比,MGFmiRNAloc在预测miRNA亚细胞局部化方面表现出更好的表现.

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  • 十倍交叉验证和独立测试实验验证实了该模型的有效性.
  • 提出的原子级特征表示成功地解决了小样本大小和短miRNA序列的局限性.

    • 结论:

    • MGFmiRNAloc提供了一种准确而强大的方法来预测miRNA亚细胞定位.
    • 原子级特征表示是分析短生物学序列的一个有希望的进步.
    • 该方法可以扩展到预测其他类型的核酸序列的亚细胞局部.