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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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相关实验视频

Updated: Jun 13, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
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基于对比学习的噪声一致的超图自编码器用于癌症 ceRNA协会预测在复杂的生物调节网络中的预测.

Xin-Fei Wang1, Lan Huang1, Yan Wang1

  • 1Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China.

Journal of chemical information and modeling
|June 12, 2025
PubMed
概括
此摘要是机器生成的。

我们开发了NCRAE,这是一种用于使用竞争性内源RNA (ceRNA) 网络预测癌症生物标志物的新框架. 这种方法通过学习强大的节点嵌入来提高预测准确性,特别是在杂的生物数据中.

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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:

  • 竞争性内源RNA (ceRNA) 网络对于理解复杂疾病中的非编码RNA作用至关重要.
  • 传统的图形模型在生物网络中的远程依赖和噪音方面扎.
  • 现有的超图模型在处理图级和节点级噪声方面存在局限性.

研究的目的:

  • 提出一个噪声一致的超图形自编码器 (NCRAE) 框架,用于在ceRNA网络中强大的节点嵌入.
  • 为了能够准确预测与癌症相关的ceRNA生物标志物.
  • 为了提高在噪声存在时的预测性能.

主要方法:

  • NCRAE使用了多视图对比学习策略,具有图表级和节点级的腐败.
  • 结合了噪声一致性损失约束,以减轻对比学习偏差并增强噪声耐受性.
  • 超图卷积和富里埃KAN技术用于有效的节点嵌入学习.

主要成果:

  • 与现有方法相比,NCRAE表现出优越的性能,特别是在噪音条件下.
  • 该框架实现了在ceRNA监管网络中嵌入学习的稳健节点.
  • 实验结果验证了NCRAE在癌症生物标志物发现方面的稳定性和预测能力.

结论:

  • NCRAE提供了一种强大的工具,用于识别与癌症相关的ceRNA生物标志物.
  • 拟议的方法有效地解决了ceRNA网络分析中的噪声挑战.
  • 在癌症生物标志物预测和发现方面,NCRAE显示出显著的实用价值.