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相关概念视频

MicroRNAs01:22

MicroRNAs

2.9K
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...
2.9K

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相关实验视频

Updated: May 13, 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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集成变压器和图形注意网络用于circRNA-miRNA相互作用预测.

Meng-Meng Wei, Lei Wang, Bo-Wei Zhao

    IEEE journal of biomedical and health informatics
    |April 15, 2025
    PubMed
    概括

    EGATCMI是一种新的计算模型,通过整合序列和全球网络特征,准确地预测circRNA-miRNA相互作用 (CMI). 这一进步有助于理解基因调节,并识别与疾病相关的分子机制.

    科学领域:

    • 计算生物学是一种计算生物学.
    • 分子生物学分子生物学
    • 生物信息学是一种生物信息学.

    背景情况:

    • 环RNA-miRNA相互作用 (CMI) 在细胞基因调节中至关重要.
    • 异常的CMI与各种疾病有关.
    • 现有的预测模型忽视了分子属性和全球网络结构.

    研究的目的:

    • 开发一个先进的计算模型来预测CMI.
    • 通过结合多功能融合来克服现有方法的局限性.

    主要方法:

    • 提出了EGATCMI,这是一个结合变压器和图形注意力网络的模型.
    • 使用Word2vec进行circRNA和miRNA序列的预训练,以捕获特征表示和相似性.
    • 使用自我注意机制从CMI网络中提取全球结构特征.

    主要成果:

    • 在基准数据集上,EGATCMI实现了高预测准确度,AUC值为0.9106和0.9470 .
    • 该模型在预测CMI方面表现优于现有的方法.
    • 案例研究显示,预测与疾病相关的miRNA-circRNA相互作用的准确率为80%.

    结论:

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    • 为了准确的CMI预测,EGATCMI有效地整合了多模式特征.
    • 该模型显示出作为生物研究和候选查工具的巨大潜力.