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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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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 10, 2025

Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres
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SSCI:自我监督的深度学习改善了癌症驱动基因识别的网络结构.

Jialuo Xu1, Jun Hao1, Xingyu Liao1

  • 1School of Computer Science, Northwestern Polytechnical University, Xi'an 710072, China.

International journal of molecular sciences
|October 16, 2024
PubMed
概括

识别癌症驱动基因对于早期检测和治疗至关重要. 这项研究引入了一种自我监督的图形卷积网络方法,以增强生物网络结构,提高癌症基因识别的准确性.

关键词:
癌症驱动基因 癌症驱动基因图表学习学习图表学习网络结构增强 网络结构增强自主监督的深度学习

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科学领域:

  • 基因组学就是基因组学.
  • 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:

  • 癌症的发病包括遗传异常.
  • 准确识别癌症基因对于早期检测和个性化医学至关重要.
  • 图表深度学习方法显示出从生物网络中识别癌症驱动基因的前景,但网络噪音和不完整性阻碍了性能.

研究的目的:

  • 提出一种用于癌症驱动基因识别的新方法.
  • 为了解决现有的图形深度学习方法的局限性,这些局限性是由杂和不完整的生物网络引起的.
  • 通过自我监督来增强生物网络结构和提高预测准确性.

主要方法:

  • 为图形卷积网络 (GCNs) 开发一个自我监督的学习框架.
  • 应用拟议的方法,自我监督癌症基因识别 (SSCI),以增强网络结构.
  • 使用标准指标评估SSCI的性能:接收器操作特征曲线 (AUROC) 下的面积,精度召回曲线 (AUPRC) 下的面积和F1分数.

主要成果:

  • 该SSCI方法实现了高可靠性,AUROC为0.966,AUPRC为0.964,F1得分为0.913.
  • 提出的方法有效地增强了生物网络结构.
  • 该方法在识别癌症驱动基因方面表现出强大的区分能力.

结论:

  • 开发的自我监督方法显著改善了癌症驱动基因识别.
  • 为了更准确的预测,SSCI提供了增强的生物网络表示.
  • 这些发现表明,SSCI具有强大的区分能力和癌症基因发现的生物解释性.