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相关概念视频

Reporter Genes02:11

Reporter Genes

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Reporter genes are a type of protein-coding gene that are often tagged to a gene of interest. Once inside a target cell, reporter genes usually produce visually identifiable characteristics like fluorescence and luminescence when expressed along with the gene of interest. Thus, reporter genes “report” the presence or absence of genes of interest in an organism, determine the gene expression pattern, or track the physical location of a DNA segment or protein in the cell.
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Chromatin Position Affects Gene Expression02:35

Chromatin Position Affects Gene Expression

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Chromatin is the massive complex of DNA and proteins packaged inside the nucleus. The complexity of chromatin folding and how it is packaged inside the nucleus greatly influences  access to genetic information. Generally, the nucleus' periphery is considered transcriptionally repressive, while the cell's interior is considered a transcriptionally active area. 
Topologically Associated Domains (TADs)
The 3-dimensional positioning of chromatin in the nucleus influences the...
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Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
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Updated: Jun 14, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
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通过使用自适应空间GNN通过组织形态预测空间分辨的基因表达.

Tianci Song1,2, Eric Cosatto2, Gaoyuan Wang3,4

  • 1Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, United States.

Bioinformatics (Oxford, England)
|September 4, 2024
PubMed
概括

从组织学图像中预测空间基因表达现在可以使用一种新的图形神经网络. 这种具有成本效益的方法提高了对健康和疾病中的组织复杂性的理解.

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科学领域:

  • 计算生物学是一种计算生物学.
  • 基因组学就是基因组学.
  • 生物信息学是一种生物信息学.

背景情况:

  • 空间转录学为组织架构和分子机制提供了洞察力,但受到高成本的限制.
  • 组织学图像是常规生成的,价格更实惠,为替代基因表达分析提供了机会.

研究的目的:

  • 开发一种可扩展的方法,从组织学图像中预测空间基因表达.
  • 为了利用组织学中的形态信息来对组织复杂性的分子解码.

主要方法:

  • 一个图形神经网络 (GNN) 框架被开发来预测空间基因表达.
  • 该模型分析组织图像以推断基因活动模式.

主要成果:

  • 在现有的最先进的方法中,GNN框架显示出更好的预测性能.
  • 在两个独立的乳腺癌队列上进行了实验,验证了该模型的有效性.
  • 该模型成功地划分了具有生物意义的空间领域.

结论:

  • 从组织学图像中预测空间基因表达是一种可行的,可扩展的替代代性昂贵的空间转录学.
  • 开发的GNN框架为剖析组织复杂性和推动生物医学研究提供了强大的工具.