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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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lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

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siRNA - Small Interfering RNAs02:30

siRNA - Small Interfering RNAs

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Small interfering RNAs, or siRNAs, are short regulatory RNA molecules that can silence genes post-transcriptionally, as well as the transcriptional level in some cases. siRNAs are important for protecting cells against viral infections and silencing transposable genetic elements.
In the cytoplasm, siRNA is processed from a double-stranded RNA, which comes from either endogenous DNA transcription or exogenous sources like a virus. This double-stranded RNA is then cleaved by the...
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piRNA - Piwi-interacting RNAs02:57

piRNA - Piwi-interacting RNAs

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PIWI-interacting RNAs, or piRNAs, are the most abundant short non-coding RNAs. More than 20,000 genes have been found in humans that code for piRNAs while only 2000 genes have been found for miRNAs. piRNAs can act at the transcriptional and post-transcriptional levels and have a vital role in silencing transposable elements present in germ cells. They are also involved in epigenetic silencing and activation. Previously, they were thought to function only in germ cells but new evidence suggests...
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Ogive Graph01:07

Ogive Graph

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An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
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Graphing Antiderivatives01:30

Graphing Antiderivatives

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The concept of an antiderivative is fundamental in calculus, describing how a function's values accumulate over time. This process is closely related to physical motion, such as the movement of a rolling ball. As the ball progresses, its position changes in response to variations in velocity, just as an antiderivative graph reflects the cumulative effect of the original function's values.Graphing an antiderivative requires interpreting how a function's values influence the shape of its...
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相关实验视频

Updated: Jan 28, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
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cncFinder:基于图形注意力网络的可解释学习模型,用于识别双功能长非编码RNA.

Qiang Tang1, Yang Yu2, Min Shen1

  • 1Key Laboratory of Non-coding RNA and Drug Discovery at Chengdu Medical College of Sichuan Province, School of Basic Medical Sciences, Chengdu Medical College, Chengdu 610500, China.

Molecular therapy. Nucleic acids
|January 26, 2026
PubMed
概括

我们开发了cncFinder,这是一种新的AI工具,可以准确识别具有编码和非编码功能的双功能长非编码RNA (lncRNA). 这一进步有助于RNA生物学研究和潜在的治疗开发.

关键词:
MT: 生物信息学 生物信息学这是一个双功能 lncRNA.有编码的和没有编码的RNA.深度学习是一种深度学习.图表注意力网络 图表注意力网络可以解释的解释性.

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RNA Pull-down Procedure to Identify RNA Targets of a Long Non-coding RNA
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Cell Based Assays of SINEUP Non-coding RNAs That Can Specifically Enhance mRNA Translation
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相关实验视频

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科学领域:

  • 分子生物学分子生物学
  • 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:

  • 长非编码RNA (lncRNAs) 具有双重的蛋白质编码和调控功能,称为双功能RNA.
  • 准确识别双功能lncRNAs对于推进RNA生物学和生物标志物发现至关重要.
  • 目前用于识别双功能lncRNA的方法需要提高准确性和范围.

研究的目的:

  • 开发和验证一种新的计算模型,cncFinder,用于准确预测双功能lncRNAs.
  • 提高对RNA多功能性及其对生物过程的影响的理解.
  • 为研究人员提供一个用户友好的工具来识别双功能lncRNAs.

主要方法:

  • 开发了cncFinder,这是一个基于图形注意力网络的模型,使用k-mer图形和Word2Vec进行特征编码.
  • 采用图表注意网络来捕捉 lncRNA 转录中的复杂序列依赖性.
  • 验证了cncFinder在独立测试数据集和跨物种数据 (老鼠,果) 上的性能.

主要成果:

  • 与测试数据集的最先进模型相比,cncFinder表现出更好的预测性能.
  • 该模型显示强度和广泛适用于不同物种.
  • 解释性分析确定了生物相关的动机,包括起始密码子和Kozak类元素,验证了其生物相关性.

结论:

  • cncFinder显著提高了双功能 lncRNA 预测的准确性和可解释性.
  • 该工具为系统发现双功能lncRNA提供了强大的资源,为RNA多功能提供了新的见解.
  • 一个用户友好的Web服务器提高了研究社区的可访问性.