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相关概念视频

Transcription Factors02:16

Transcription Factors

82.9K
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...
82.9K
Diffusion01:12

Diffusion

221.8K
Diffusion is the passive movement of substances down their concentration gradients—requiring no expenditure of cellular energy. Substances, such as molecules or ions, diffuse from an area of high concentration to an area of low concentration in the cytosol or across membranes. Eventually, the concentration will even out, with the substance moving randomly but causing no net change in concentration. Such a state is called dynamic equilibrium, which is essential for maintaining overall...
221.8K
Diffusion01:21

Diffusion

6.6K
Diffusion is a type of passive transport. In passive transport, a substance tends to move from an area of high concentration to an area of low concentration until the concentration is equal across the space. For example, take the diffusion of substances through the air. When someone opens a perfume bottle in a room filled with people, the perfume is at its highest concentration in the bottle and is at its lowest at the edges of the room. The perfume vapor will diffuse, or spread away, from the...
6.6K
Transcription01:10

Transcription

157.1K
Overview
Transcription is the process of synthesizing RNA from a DNA sequence by RNA polymerase. It is the first step in producing a protein from a gene sequence. Additionally, many other proteins and regulatory sequences are involved in the proper synthesis of messenger RNA (mRNA). Regulation of transcription is responsible for the differentiation of all the different types of cells and often for the proper cellular response to environmental signals.
Transcription Can Produce Different Kinds...
157.1K
Transcription Elongation Factors02:35

Transcription Elongation Factors

14.1K
Transcription elongation is a dynamic process that alters depending upon the sequence heterogeneity of the DNA being transcribed. Hence, it is not surprising that the elongation complex's composition also varies along the way while transcribing a gene.
The transcription elongation is regulated via pausing of RNA polymerase on several occasions during transcription. In bacteria, these halts are necessary because the transcription of DNA into mRNA is coupled to the translation of that mRNA...
14.1K
Eukaryotic Transcription Activators02:42

Eukaryotic Transcription Activators

12.9K
Transcription activators are proteins that promote the transcription of genes from DNA to RNA. In most cases, these proteins contain two separate domains ‒ a domain that binds to DNA and a domain for activating transcription; however, in some cases, a single domain is responsible for both binding and activation of transcription, as seen in the glucocorticoid receptor and MyoD.
The binding domains are capable of recognizing and interacting with regulatory sequences on the DNA. These...
12.9K

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相关实验视频

Updated: Feb 13, 2026

Rapid in vivo Drug Response Prediction Using Leukemia Cell Grafts in Zebrafish Embryos
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使用多条件扩散变压器预测药物扰乱的转录反应.

Qifan Hu1, Zeyu Chen1, Jin Gu1

  • 1MOE Key Laboratory of Bioinformatics BNRIST Bioinformatics Division Department of Automation Tsinghua University Beijing China.

Quantitative biology (Beijing, China)
|February 12, 2026
PubMed
概括
此摘要是机器生成的。

我们开发了一种新的扩散变压器模型PertDiT,以高效地产生药物扰乱的转录组. 这种人工智能方法通过比现有方法更准确地预测转录变化,加速药物发现和个性化医疗.

关键词:
扩散模型的扩散模型.干扰是一种干扰.转录组 (transcriptome) 是一个转录组.

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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.
  • 人工智能的人工智能

背景情况:

  • 药物扰乱的转录组对于个性化医学和药物发现至关重要.
  • 目前的高通量选方法昂贵且耗时.

研究的目的:

  • 引入一种新的多条件扩散变压器模型,PertDiT,用于生成扰乱的转录组.
  • 为了实现基于药物文本信息的转录基因组的条件生成.

主要方法:

  • 开发了 PertDiT,将变压器架构与大型语言模型集成为文本表示.
  • 嵌入了新的扰动和转录组融合模块.
  • 为药物发现和个性化医学设计了CrossDiT和CatCrossDiT网络结构.

主要成果:

  • PertDiT在扰动后的转录组重建中表现出卓越的性能.
  • 该模型准确地预测了扰动诱导的转录变化.
  • 在综合指标和数据分割策略中表现优于现有方法.

结论:

  • PertDiT提供了一种高效和有效的方法来生成药物扰乱的转录组.
  • 该模型显示了对推进药物发现和个性化医学的重大前景.
  • 该模型的架构和有效性经过了严格的验证.