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相关概念视频

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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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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相关实验视频

Updated: Jul 22, 2025

mirMachine: A One-Stop Shop for Plant miRNA Annotation
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基于多源数据融合的生成对抗矩阵完成网络,用于预测miRNA-疾病关联预测.

ShuDong Wang1, YunYin Li1, YuanYuan Zhang1

  • 1College of Computer Science and Technology, Qingdao Institute of Software, China University of Petroleum (East China), 66 Changjiang Xi Lu, 266580, Shandong, China.

Briefings in bioinformatics
|July 23, 2023
PubMed
概括

这项研究介绍了GAMCNMDF,这是一个新的计算模型,通过合并各种数据源来预测微RNA-疾病关联. 它在识别与疾病相关的miRNA和潜在的治疗点方面表现出卓越的性能.

关键词:
产生性的对抗性网络.完成矩阵的完成.微RNA与疾病的关联类似性网络的类似性网络.

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相关实验视频

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科学领域:

  • 生物医学信息学 生物医学信息学
  • 计算生物学 计算生物学
  • 基因组学就是基因组学.

背景情况:

  • 微RNAs (miRNAs) 在复杂疾病中至关重要,作为潜在的生物标志物和治疗点.
  • 计算方法越来越多地用于识别与疾病相关的miRNAs.
  • 现有模型面临的局限性是由于数据融合不足和关联知识不完整.

研究的目的:

  • 开发一种先进的计算模型,用于预测miRNA与疾病的关联.
  • 克服以前模型在数据融合和处理不完整信息方面的局限性.
  • 提高识别与疾病相关的miRNA的准确性和可靠性.

主要方法:

  • 提出基于多源数据融合 (GAMCNMDF) 的生成对抗矩阵完成网络.
  • 整合不同的数据源与非线性融合方法来更新miRNA和疾病相似性网络.
  • 使用"暗示"机制,以在不完整的数据上实现成功的预测.

主要成果:

  • 在两个数据库的10倍交叉验证中,GAMCNMDF表现出卓越的性能.
  • 该模型在识别小分子相关的miRNAs方面取得了杰出的结果.
  • 关于瘤的案例研究证实了GAMCNMDF作为一个有前途的预测工具.

结论:

  • GAMCNMDF提供了一种强大而有效的方法来预测miRNA与疾病的关联.
  • 该模型能够整合多样化的数据和处理不完整的信息,提高了其适用性.
  • GAMCNMDF显示了促进疾病诊断和治疗标识的重大潜力.