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相关概念视频

Combinatorial Gene Control02:33

Combinatorial Gene Control

8.4K
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...
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Cis-regulatory Sequences02:02

Cis-regulatory Sequences

10.1K
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...
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相关实验视频

Updated: Sep 11, 2025

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
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Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

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diffMIN:基于相互信息的样本特定差异基因监管网络的重建.

Songwei Ai, Yiwei Zhou, Wenkui Zou

    IEEE transactions on computational biology and bioinformatics
    |August 14, 2025
    PubMed
    概括

    这项研究介绍了diffMIN,这是一种创建样本特定基因调节网络的新方法. diffMIN增强了细胞聚类,并识别了疾病预后的关键基因,优于现有的方法.

    科学领域:

    • 基因组学和系统生物学
    • 计算生物学 计算生物学
    • 生物信息学是一种生物信息学.

    背景情况:

    • 基因调节机制是生物系统的基础.
    • 样本特定网络 (SSN) 提供了对生物过程的精确见解.
    • 目前用于SSN推断的方法仅限于单个样本.

    研究的目的:

    • 开发一种新的计算方法来重建样本特定的差异基因调节网络.
    • 评估新方法在细胞聚类和基因鉴定中的性能.
    • 应用该方法来分析心血管和癌症数据集.

    主要方法:

    • 拟议的diffMIN (差异性相互信息) 用于样本特定的差异性基因调控网络重建.
    • 利用相互信息进行网络推断.
    • 将diffMIN应用于单细胞RNA测序数据集,包括横向大动脉收缩 (TAC) 和癌症基因组图谱 (TCGA) 乳腺癌数据.

    主要成果:

    • 与现有的SSN算法相比,diffMIN在细胞聚类方面表现优越.
    • 对TAC数据的分析揭示了心力衰竭进展过程中调节网络的单调变化.
    • 对乳腺癌数据的应用确定了与患者预后相关的关键基因,即使没有显著的差异表达.

    更多相关视频

    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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    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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    Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
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    Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

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    Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
    10:44

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

    Published on: December 7, 2021

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    Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
    03:37

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

    Published on: March 1, 2024

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    结论:

    • diffMIN是一种广泛适用的方法,用于构建样本特定的差分网络.
    • 该方法在细胞聚类,识别关键基因,调查生物过程和帮助疾病诊断方面表现出色.
    • diffMIN为个性化医疗和理解复杂疾病提供了一个强大的工具.