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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

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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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Tagging and Fusion Proteins01:24

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Proteins are involved in several cellular processes and biochemical reactions. Analyzing a specific protein of interest requires it to be isolated from the other proteins in the cell. This is achieved by overexpressing the specific gene in a suitable host to produce large quantities of the target protein. A tag or label is recombined with the gene to produce a fusion protein containing the target protein and the tag. The tags on these fusion proteins can then be used for easy detection and...
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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Multi-input and Multi-variable systems01:22

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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相关实验视频

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mirMachine: A One-Stop Shop for Plant miRNA Annotation
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AttMVGraph:基于注意力的多模式融合和变化图学习,用于SM-miRNA协会预测.

Ran Tao1, Weizhong Lu1, Hongjie Wu1

  • 1The School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China.

Journal of chemical information and modeling
|December 9, 2025
PubMed
概括

本研究介绍了AttMVGraph,这是一种用于预测小分子 (SM) 和微RNA (miRNA) 之间的相互作用的新计算方法. 该模型通过整合多式联运数据和先进的图形学习技术来提高准确性.

科学领域:

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

背景情况:

  • 微RNA (miRNA) 是调节基因表达的关键非编码RNA,是治疗点.
  • 实验识别小分子 (SM) -miRNA相互作用是昂贵和低效的.
  • 开发准确的预测模型对于推进基于SM-miRNA的治疗方法至关重要.

研究的目的:

  • 提出一种有效的计算方法,AttMVGraph,用于预测SM-miRNA关联.
  • 为了利用基于注意力的多式联络融合和变量图的学习来提高预测准确性.
  • 为了应对数据不平衡的挑战,并提高模型的稳定性.

主要方法:

  • 使用随机步行与重启 (RWR) 进行拓特征和SM/miRNA相似性作为多式输入.
  • 用于适应加权融合和歧视图嵌入的功能增强通道注意力 (FECA).
  • 集成了一个变量图自编码器 (VGAE) 用于不确定性建模和表示学习.
  • 集成的动态硬负采矿 (DHNM) 处理样本不平衡并加强决策边界.

主要成果:

  • 在5个交叉验证中,AttMVGraph取得了很好的表现.
  • 在数据集1上获得的AUC值为0.9937 ± 0.0061,在数据集2上达到0.9727 ± 0.0001.

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  • 在数据集1上取得的AUPR值为0.9397 ± 0.0753,在数据集2上达到0.8807 ± 0.0589.
  • 证明了拟议的AttMVGraph模型的有效性和优越性.

    • 结论:

    • AttMVGraph显著改善了SM-miRNA关联的预测.
    • 该模型的架构有效地处理多式联运数据和复杂的关系.
    • 这些发现支持AttMVGraph在加速SM-miRNA疗法的发现方面的实用性.