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相关概念视频

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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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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Types of RNA01:20

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Three main types of RNA are involved in protein synthesis: messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). These RNAs perform diverse functions and can be broadly classified as protein-coding or non-coding RNA. Non-coding RNAs play important roles in regulating gene expression in response to developmental and environmental changes. Non-coding RNAs in prokaryotes can be manipulated to develop more effective antibacterial drugs for human or animal use.
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RNA Interference01:23

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RNA interference (RNAi) is a process in which a small non-coding RNA molecule blocks the post-transcriptional expression of a gene by binding to its messenger RNA (mRNA) and preventing the protein from being translated.
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相关实验视频

Updated: Jan 14, 2026

Identification of RNAs Engaged in Direct RNA-RNA Interaction with a Long Non-Coding RNA
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生物LLMNet:通过专门的跨LLM转换网络增强RNA相互作用预测.

Abrar Rahman Abir1, Md Toki Tahmid1, Md Shamsuzzoha Bayzid1

  • 1Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka 1000, Bangladesh.

Briefings in bioinformatics
|October 26, 2025
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概括
此摘要是机器生成的。

一个新的框架BioLLMNet准确地预测了与蛋白质,小分子和其他RNA的核糖核酸 (RNA) 相互作用,仅使用序列数据. 这通过改进RNA相互作用预测的计算方法来推进RNA向疗法.

关键词:
预测RNA相互作用的预测生物学语言模型的模型.多式代表学习学习多式代表学习

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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.
  • 分子生物学分子生物学

背景情况:

  • 核糖核酸 (RNA) 对于细胞功能至关重要,与蛋白质,小分子和其他RNA相互作用.
  • 预测RNA相互作用对于理解基因调节和开发基于RNA的疗法至关重要.
  • 目前的计算方法面临由于特征工程,模式特定模型和数据要求的限制.

研究的目的:

  • 开发一个统一的,仅序列的计算框架,用于预测各种RNA相互作用.
  • 通过利用预训练的生物语言模型来克服现有方法的局限性.
  • 为了能够准确和可概括地预测RNA-蛋白,RNA-小分子和RNA-RNA相互作用.

主要方法:

  • 介绍了BioLLMNet,一个统一的仅序列框架,使用预训练的生物语言模型.
  • 开发了一种新的可学习的门机制,用于多式联运嵌入的自适应集成.
  • 启用了功能智能权重的动态计算,以强调上下文相关的信息.

主要成果:

  • 在RNA-蛋白,RNA-小分子和RNA-RNA相互作用预测任务中,BioLLMNet实现了最先进的性能.
  • 与现有方法相比,该框架显示出更高的通用性和准确性.
  • 可学习的关门机制有效地融合了异构的交互模式.

结论:

  • 生物LLMNet提供了一个统一的,基于序列的方法,用于全面的RNA相互作用建模.
  • 该研究强调了语言模型嵌入和动态特征融合在RNA相互作用预测中的有效性.
  • 这项工作推动了RNA向治疗和转录后调节研究的发展.