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相关概念视频

Epigenetic Regulation01:37

Epigenetic Regulation

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Epigenetic changes alter the physical structure of the DNA without changing the genetic sequence and often regulate whether genes are turned on or off. This regulation ensures that each cell produces only proteins necessary for its function. For example, proteins that promote bone growth are not produced in muscle cells. Epigenetic mechanisms play an essential role in healthy development. Conversely, precisely regulated epigenetic mechanisms are disrupted in diseases like cancer.
X-chromosome...
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Master transcription regulators are regulatory proteins that are predominantly responsible for regulating the expression of multiple genes. Often these genes work in concert to drive a  complex process. Activation of a master transcription regulator can lead to a cascade of transcriptional activation necessary for that outcome. These regulators can directly bind to the regulatory sequences of the various genes involved, or they can indirectly regulate transcription by binding to regulatory...
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相关实验视频

Updated: Jun 4, 2025

Pattern-based Search of Epigenomic Data Using GeNemo
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EpiGePT:一种预训练的基于变压器的语言模型,用于特定环境的人类表观遗传学.

Zijing Gao1, Qiao Liu2, Wanwen Zeng3

  • 1Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing, 100084, China.

Genome biology
|December 19, 2024
PubMed
概括

一个新的模型EpiGePT通过整合3D基因组相互作用和转录因子活动来预测人类表观基因组信号. 这促进了对不同细胞类型的基因调节的理解.

关键词:
三维基因组是3D的表观基因组学是指表观基因组学.基因规则 基因规则语言模型 语言模型变压器变压器变压器

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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
  • 表观遗传学 在表观遗传学中,表观遗传学是指表观遗传学.

背景情况:

  • 大型语言模型 (LLM) 在基因组学中显示出希望,因为自然语言和生物序列之间的相似性.
  • 现有的LLM在预测表观基因信号方面面临局限性,特别是关于染色质相互作用和未见的细胞环境.
  • 了解细胞类型特定的基因调节对于破译复杂的生物过程至关重要.

研究的目的:

  • 开发一种新的基于变压器的模型,EpiGePT,用于准确预测特定环境的人类表观遗传信号.
  • 通过结合3D基因组架构和转录因子动态来增强基因组学LLM的预测能力.
  • 改善基因变异对不同细胞环境中的基因调节的影响的预测.

主要方法:

  • 开发了EpiGePT,这是一个整合转录因子活动和3D基因组相互作用数据的变压器架构.
  • 在人类表观基因组数据集上训练和评估EpiGePT,专注于细胞类型特定信号预测.
  • 将EpiGePT的性能与用于表观基因组预测任务的现有最先进方法进行了比较.

主要成果:

  • 与目前的方法相比,EpiGePT在预测特定环境的人类表观遗传信号方面表现出卓越的表现.
  • 该模型在预测细胞类型特定的长距离色素相互作用方面表现出色.
  • EpiGePT准确地预测了遗传变异对表观基因组调节的影响.

结论:

  • 通过有效地整合各种基因组数据,包括染色体相互作用,EpiGePT推进了表观基因组信号的预测.
  • 该模型为了解细胞类型特定的基因调节和遗传变异的功能后果提供了一个强大的工具.
  • 开发的预测服务为基因组学和表观遗传学研究社区提供了宝贵的资源.