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相关概念视频

Cis-regulatory Sequences02:02

Cis-regulatory Sequences

9.8K
Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
9.8K
Regulation of Expression at Multiple Steps01:23

Regulation of Expression at Multiple Steps

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The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the...
875
Combinatorial Gene Control02:33

Combinatorial Gene Control

8.3K
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.
The expression of more than 30,000 genes is controlled by approximately 2000-3000 transcription factors. This is possible because a single transcription factor can recognize more than one regulatory sequence. The specificity in gene...
8.3K
Inheritance01:25

Inheritance

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Gregor Mendel's pioneering work on the principles of inheritance fundamentally transformed our understanding of how traits are transmitted from generation to generation. His experiments with pea plants laid the groundwork for the discovery of genes, discrete units within organisms that control heredity.
Each gene exists in pairs, and the combination of these genes from both parents forms an individual's genotype. This genotype is a blueprint of potential traits. Examples of genotype...
346
Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

6.4K
Transcriptional regulators bind to specific cis-regulatory sequences in the DNA to regulate gene transcription. These cis-regulatory sequences are very short, usually less than ten nucleotide pairs in length. The short length means that there is a high probability of the exact same sequence randomly occurring throughout the genome.  Since regulators can also bind to groups of similar sequences, this further increases the chances of random binding. Transcriptional regulators form...
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Neural Regulation01:37

Neural Regulation

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Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
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相关实验视频

Updated: Jun 11, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

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从噪音到知识:基因调控网络的扩散概率模型基础的神经推理.

Hao Zhu1, Donna Slonim1

  • 1Department of Computer Science, Tufts University, Medford, Massachusetts, USA.

Journal of computational biology : a journal of computational molecular cell biology
|October 10, 2024
PubMed
概括

RegDiffusion是一种新的传播概率模型,从单细胞数据中准确地推断出基因调节网络 (GRNs). 这种方法为了解细胞机制和开发新疗法提供了卓越的性能和速度.

科学领域:

  • 计算生物学 计算生物学
  • 基因组学就是基因组学.
  • 系统生物学 系统生物学

背景情况:

  • 基因调节网络 (GRNs) 对于理解细胞功能和开发向疗法的必要.
  • 使用批量表达数据的传统GRN推断方法由于高维度和噪声而面临挑战.
  • 准确的GRN推断对于推动生物研究和治疗策略至关重要.

研究的目的:

  • 介绍RegDiffusion,一种用于推断基因调节网络的新型计算方法.
  • 评估RegDiffusion的性能与现有的GRN推断技术相比.
  • 展示RegDiffusion对于分析大规模单细胞表达数据的实用性.

主要方法:

  • RegDiffusion采用一个针对GRN推断量身定制的Denoising Diffusion概率模型.
  • 该模型将高斯噪声引入表达数据,并利用具有参数化的邻近矩阵的神经网络来学习调节关系.
  • 简化的神经网络架构用于高效的噪音预测.

主要成果:

  • 与基线方法相比,RegDiffusion在跨多个基准数据集的GRN推断中实现了卓越的性能.
  • 该模型成功地从复杂的单细胞RNA测序数据中推断出具有生物意义的GRNs.
关键词:
网络推断 网络推断单细胞分析和监管系统

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  • RegDiffusion表现出了显著的效率,在不到5分钟的时间内从超过15,000个基因的数据集中推断出网络.
  • 结论:

    • RegDiffusion 为基因调节网络推断提供了一种有效和计算效率高的方法.
    • 基于扩散的模型显示出对推进单细胞数据分析和生物发现的重大前景.
    • 开发的RegDiffusion工具有助于研究细胞系统中复杂的基因调节.