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相关概念视频

Cell Specific Gene Expression01:58

Cell Specific Gene Expression

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Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
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Updated: Jul 15, 2025

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations
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基于深度学习的单细胞表达预测的同质空间构造和投影.

Chia-Hung Yeh1,2, Ze-Guang Chen1, Cheng-Yue Liou1

  • 1Department of Electrical Engineering, National Taiwan Normal University, Taipei 10610, Taiwan.

Bioengineering (Basel, Switzerland)
|September 28, 2023
PubMed
概括

由于细胞类型的差异,预测细胞对干扰的反应具有挑战性. 拟议的信息导航变量自编码器 (INVAE) 通过过不相关信息并创建统一的特征空间来提高预测准确性.

关键词:
预测细胞扰动反应的预测细胞响应均的空间建设.深度学习是一种深度学习.解开纠的表示形式.可以解释的解释性.

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科学领域:

  • 计算生物学 计算生物学
  • 基因组学就是基因组学.
  • 机器学习在生物学中的应用

背景情况:

  • 预测细胞对干扰的反应至关重要,但仍然具有挑战性.
  • 现有的方法经常失败,因为假设细胞类型之间的反应相似,忽视了细胞类型特定的基因组相互作用.
  • 深度学习模型与生物数据的异质性作斗争,导致定义不佳的潜在空间和域特定映射.

研究的目的:

  • 开发一种新的深度学习框架,用于准确预测细胞对干扰的反应.
  • 为了应对数据异质性和细胞类型特定扰动预测领域转移的挑战.
  • 通过识别保存的调节模式,提高细胞反应机制的解释性.

主要方法:

  • 介绍了信息导航变量自编码器 (INVAE),这是一个用于扰乱响应预测的深度神经网络.
  • INVAE采用信息过机制来删除非必要的生物数据.
  • 该模型构建了一个同质的控制条件空间,并将其映射到扰动条件空间,从而实现跨域预测.

主要成果:

  • 与三个现实数据集中的三种最先进的方法相比,INVAE在细胞状态预测方面表现出了卓越的表现.
  • 信息过方法显著提高了预测准确性.
  • 分析显示,过不相关的信息突出显示了不同细胞类型中保存的基因调节相似性.

结论:

  • INVAE提供了一种强大而准确的方法来预测细胞对干扰的反应,其性能优于现有的方法.
  • 该模型处理数据异质性和域转移的能力使其适用于各种生物应用.
  • INVAE提供了对保存的基因调节机制的洞察,进步了我们对细胞反应的理解.