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相关概念视频

Experimental RNAi02:15

Experimental RNAi

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RNA interference (RNAi) is a cellular mechanism that inhibits gene expression by suppressing its transcription or activating the RNA degradation process. The mechanism was discovered by Andrew Fire and Craig Mello in 1998 in plants. Today, it is observed in almost all eukaryotes, including protozoa, flies, nematodes, insects, parasites, and mammals. This precise cellular mechanism of gene silencing has been developed into a technique that provides an efficient way to identify and determine the...
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RNA Interference01:23

RNA Interference

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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.
This process occurs naturally in cells, often through the activity of genomically-encoded microRNAs. Researchers can take advantage of this mechanism by introducing synthetic RNAs to deactivate specific genes for research or therapeutic purposes. For example, RNAi could be used...
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Types of RNA01:23

Types of RNA

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Overview
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 the regulation of 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.
RNA...
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Types of RNA01:20

Types of RNA

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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.
RNA Performs Diverse...
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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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lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

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相关实验视频

Updated: Feb 28, 2026

RNA Secondary Structure Prediction Using High-throughput SHAPE
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RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

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使用人工智能预测非编码RNA功能

David da Costa Correia1, Francisco M Couto2, Hugo Martiniano3

  • 1Departamento de Promoção da Saúde e Prevenção de Doenças não Transmissiveis, Instituto Nacional de Saúde Doutor Ricardo Jorge, Avenida Padre Cruz, Lisboa, 1649-016, Portugal; BioISI - Biosystems and Integrative Sciences Institute, Faculdade de Ciências da Universidade de Lisboa, Campo Grande, Lisboa, 1749-016, Portugal; Departamento de Informática, Faculdade de Ciências da Universidade de Lisboa, Campo Grande, Lisboa, 1749-016, Portugal; LASIGE, Faculdade de Ciências da Universidade de Lisboa, Campo Grande, Lisboa, 1749-016, Portugal.

Journal of biomedical informatics
|February 25, 2026
PubMed
概括
此摘要是机器生成的。

这项研究开发了一种方法,利用自然语言处理 (NLP) 和大型语言模型 (LLM) 从科学文献中提取非编码RNA (ncRNA) 和表型关系. 该方法获得了高F1分数,对未来的ncRNA研究具有前景.

关键词:
远程监督 远程监督 远程监督大型语言模型没有编码的RNAs.关系提取 关系提取文本挖掘 (Text Mining) 是一种文字挖掘方式.

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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
  • 基因组学就是基因组学.

背景情况:

  • 非编码RNAs (ncRNAs) 在生物过程和疾病中起着至关重要的作用.
  • 关于ncRNA-表型关系的信息在科学文献中是分散的.
  • 需要有效的方法来汇总和规范这些分散的数据.

研究的目的:

  • 开发一种方法来从科学文章中提取ncRNA-表型关系.
  • 为了完成这一任务,将自然语言处理 (NLP) 和大型语言模型 (LLM) 结合起来.
  • 为ncRNA研究创建一个高可靠性数据集和关系群.

主要方法:

  • 开发了一个NLP管道来汇总和规范五个ncRNA疾病数据库的数据.
  • 使用远程监督关系提取 (DSRE) 生成一个ncRNA-表型关系体.
  • 应用大型语言模型 (LLM) 用于关系提取 (RE),评估对验证的语料库子集的性能.

主要成果:

  • 创建了一个高保真性ncRNA-表型关系数据集,包含214,300个关系.
  • 从21,608个文章中生成了一个关系群体 (ncoRP),包含35,295个独特的关系.
  • 使用基于LLM的RE方法获得0.978的高F1得分.

结论:

  • 成功创建了一个规范化的ncRNA-表型数据集和关系体.
  • 合并的LLM和DSRE方法证明了自动关系提取的高性能.
  • 开发的数据集,数据库和方法是ncRNA研究的宝贵资源,可以应用于类似的生物关系提取任务.