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相关概念视频

lncRNA - Long Non-coding RNAs02:39

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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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Genes usually encode proteins necessary for the proper functioning of a healthy cell. Mutations can often cause changes to the gene expression pattern, thereby altering the phenotype.
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HGCPep:超图的深度学习识别了与癌症相关的非编码.

Wentao Long1,2, Zhongshen Li1,2, Junru Jin1,2

  • 1School of Software, Shandong University, Jinan 250101, China.

Genomics, proteomics & bioinformatics
|December 2, 2025
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概括

一个新的深度学习框架,HGCPep,通过考虑它们来自非编码RNA (ncRNA) 的共同起源来识别与癌症相关的非编码 (ncPEP). 这种方法改善了新型癌症生物标志物和瘤学治疗点的发现.

关键词:
癌症生物标志物 癌症生物标志物超图形学习的学习方法多个标签分类的分类.体特征表示表示体特征表示在 ncPEP 识别中使用 ncPEP 识别.

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科学领域:

  • 在瘤学瘤学.
  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学

背景情况:

  • 由非编码RNAs (ncRNAs) 编码的非编码 (ncPEP) 正在成为癌症中的关键调节剂和生物标志物.
  • 目前ncPEP识别的计算方法主要依赖于序列分析,忽视了单个ncRNA中的的共享转录起源.

研究的目的:

  • 开发一种新的深度学习框架,HGCPep,该框架模拟ncRNAs及其编码的ncPEPs之间的内在关系.
  • 通过结合转录背景来改善与癌症相关的ncPEPs的系统识别.

主要方法:

  • 开发了HGCPep,这是一个深度学习框架,利用超图来表示ncRNA及其编码的.
  • 整合了超图神经网络与卷积神经网络,以丰富特征表示与转录上下文.
  • 应用了对已学习的嵌入的维度缩小来分析ncPEP按癌症类型的聚类.

主要成果:

  • 在识别与癌症相关的ncPEP方面,HGCPep的性能优于最先进的方法.
  • 来自HGCPep的学习嵌入揭示了基于癌症类型的ncPEPs的独特集群,表明有效破译生物关联.
  • 该框架为在瘤学中发现新的治疗点提供了一个强大的工具.

结论:

  • 通过利用超图形建模,HGCPep提供了一种新且有效的ncPEP分析方法.
  • 该框架加强了与癌症相关的ncPEPs的识别,并有助于理解复杂的生物关联.
  • HGCPep在发现癌症免疫治疗的新疗法点方面取得了重大进展.