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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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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...
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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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相关实验视频

Updated: Jan 8, 2026

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
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多头超图卷积与特征增强和潜伏表示学习为miRNA-疾病协会预测.

Pengli Lu, Zhong Yan, Fentang Gao

    IEEE transactions on computational biology and bioinformatics
    |December 18, 2025
    PubMed
    概括

    本研究介绍了FKAMHV,这是一个用于预测miRNA疾病关联的新框架,通过整合快速Kolmogorov-Arnold网络和多头超图卷积网络来捕获复杂的拓结构,显著提高稀疏数据场景的准确性.

    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
    • 网络医学 网络医学

    背景情况:

    • 微RNA (miRNA) 与疾病的关联对于理解疾病机制至关重要.
    • 现有的计算方法难以处理稀疏的数据和捕捉深层拓结构.

    研究的目的:

    • 开发一个新的框架,FKAMHV,用于强大的miRNA疾病关联预测.
    • 加强深层拓特征的提取,并揭示潜在的关联,特别是在稀疏条件下.

    主要方法:

    • 构建异质网络并生成miRNA/特定疾病的超图.
    • 集成的快速科尔摩戈罗夫-阿诺德网络 (FastKAN) 用于非线性特征建模和多头超图卷积网络 (多头HGCN) 用于联合表示.
    • 采用 $\beta$-变量自编码器 ($\beta$-VAE) 进行潜在关联建模,并在HGCN中引入了注意力机制和跳跃知识策略.

    主要成果:

    • 在曲线下的面积 (AUC) 和精度召回曲线下的面积 (AUPR) 方面,FKAMHV在现有方法上表现出优越的性能.
    • 该框架实现了强大的预测性能,即使与稀疏的关联数据.

    结论:

    • FKAMHV有效地捕获复杂的拓结构和潜在的关联,用于miRNA疾病预测.

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  • 拟议的方法提供了更好的概括性和稳定性,特别是在稀疏的数据设置中,推进了计算疾病关联研究领域.