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相关概念视频

Forced Transdifferentiation01:28

Forced Transdifferentiation

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Transdifferentiation, also known as lineage reprogramming, was first discovered by Selman and Kafatos in 1974 in silkmoths. They observed that the moths’ cuticle-producing cells transformed into salt-producing cells. Many such cases of natural transdifferentiation occur in organisms. In humans, pancreatic alpha cells can become beta cells. In newts, the loss of the eye’s lens causes the pigmented epithelial cells to transdifferentiate into the lens cells.
Artificial...
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Master Transcription Regulators02:23

Master Transcription Regulators

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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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Methods of Nuclear Reprogramming01:24

Methods of Nuclear Reprogramming

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Nuclear reprogramming is a process of transforming one cell type into an unrelated cell type by epigenetic changes that alter the cell’s original gene expression pattern. Such epigenetic changes force cells to express a different set of genes, which play a significant role in inducing transformation into other cell types. Nuclear reprogramming offers applications in reproductive cloning for livestock propagation and regenerative medicine — developing patient-specific cells for...
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Combinatorial Gene Control02:33

Combinatorial Gene Control

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Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
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General Transcription Factors01:30

General Transcription Factors

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Tissue-specific transcription factors contribute to diverse cellular functions in mammals. For example, the gene for beta globin, a major component of hemoglobin, is present in all cells of the body. However, it is only expressed in red blood cells because the transcription factors that can bind to the promoter sequences of the beta globin gene are only expressed in these cells. Tissue-specific transcription factors also ensure that mutations in these factors may impair only the function of...
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相关实验视频

Updated: May 30, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
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Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

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一个多式变压器用于细胞类型-不可知性监管预测.

Nauman Javed1, Thomas Weingarten2, Arijit Sehanobish3

  • 1The Gene Regulation Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.

Cell genomics
|January 30, 2025
PubMed
概括

新的深度学习模型EpiBERT通过整合序列和细胞特定数据来增强基因组预测. 这种方法改善了对调控基因组学研究的新细胞类型的概括.

关键词:
染色质可访问性 染色质可访问性深度学习是一种深度学习.基因调节 基因调节 基因调节基因组学就是基因组学.序列代码是一个序列代码.变压器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:

  • 深度学习模型擅长分析基因组序列的cis-regulatory元素.
  • 目前的模型很难将预测推广到不同的细胞环境中.
  • 了解基因调节需要将序列信息与细胞类型特定数据集成在一起.

研究的目的:

  • 开发一种深度学习模型,用于调控基因组学,在细胞环境中进行概括.
  • 创建一个多模转换器,将基因组序列和染色质可访问性数据结合起来.
  • 提高监管基因组学模型的可解释性和预测能力.

主要方法:

  • 开发了EpiBERT,一种多模式变压器模型.
  • 采用了基于可访问性的掩盖预培训目标.
  • 微调EpiBERT用于基因表达预测和其他调控基因组学任务.

主要成果:

  • 埃皮伯特实现了基因表达预测准确度,与像Enformer这样的仅序列模型相美.
  • 埃皮伯特证明了对未观察到的细胞状态的显著概括.
  • 学习的表征是可解释的,有助于预测caQTLs,动机和增强器基因链接.

结论:

  • 在为监管基因组学创建可概括的深度学习模型方面,EpiBERT代表了重大进展.
  • 整合多模式数据可以提高模型的性能和适用于各种不同的蜂环境.
  • 这项工作为基因组学中更强大的基于序列的深度学习铺平了道路.