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The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the...
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从单细胞转录组中推断基因调节网络,使用图形自编码模型.

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科学领域:

  • 基因组学就是基因组学.
  • 计算生物学 计算生物学
  • 系统生物学 系统生物学

背景情况:

  • 基因调节网络对于细胞功能至关重要,涉及复杂的多基因相互作用.
  • 当前的单细胞基因调节网络推断方法往往忽视全球结构,只关注对对基因关系.
  • 了解全球监管架构对于破译复杂的生物系统至关重要.

研究的目的:

  • 开发一种基于图形的新型深度学习模型,从单细胞RNA-seq数据推断基因调节网络.
  • 通过整合全球监管结构来解决现有方法的局限性.
  • 准确地重建基因调节网络并识别新型调节器.

主要方法:

  • 提出了DeepRIG (监管网络的深度学习模型,基因之间的推理),一种基于图形的深度学习方法.
  • 通过将基因表达数据转换为共表达模式,构建了一个先前的调节图.
  • 利用图形自编码器将全球监管信息嵌入到基因潜伏嵌入中以重建网络.

主要成果:

  • DeepRIG准确地重建了基因调节网络.
  • 与模拟和真实生物网络上的现有方法相比,表现出优异的性能.
  • 成功应用于人类外周血液单核细胞和三阴性乳腺癌样本.

结论:

  • DeepRIG提供了精确的细胞类型特定基因调节网络推断.
  • 该模型可以识别参与疾病进展和抑制的新型调节剂.
  • 这种方法增强了对细胞系统中复杂的基因调节机制的理解.