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相关概念视频

Combinatorial Gene Control02:33

Combinatorial Gene Control

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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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Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

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Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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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...
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相关实验视频

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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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基因调控网络推断基于新型组合方法的推断.

Bin Yang1, Jing Li1, Xiang Li2

  • 1School of Information Science and Engineering, Zaozhuang University, No. 1 Beian Road, Zaozhuang 277160, China.

Briefings in functional genomics
|September 26, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种使用灵活的神经树和13个分类器来增强基因调节网络 (GRN) 识别的集合方法. 这种新的方法显著提高了理解基因功能和疾病发展的准确性.

关键词:
这是分类分类的分类.灵活的神经树 灵活的神经树基因监管网络 基因监管网络一个单细胞RNA-seqq.

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科学领域:

  • 计算生物学是一种计算生物学.
  • 生物信息学是一种生物信息学.
  • 系统生物学 系统生物学

背景情况:

  • 基因调节网络 (GRNs) 对于理解基因功能,疾病发展和癌症至关重要.
  • 准确的GRN识别对于推动生物和医学研究至关重要.

研究的目的:

  • 开发一种先进的合并方法,以提高GRN识别准确度.
  • 提高对基因功能及其在癌症等疾病中的作用的理解.

主要方法:

  • 一种组合方法,将13个基本分类算法 (例如,随机森林,XGBoost,SVM) 与灵活的神经树 (FNT) 结合起来.
  • 一种混合进化算法,整合了基因编程和粒子群优化,以优化FNT模型.
  • 使用模拟数据集和真实单细胞RNA-seq数据进行验证.

主要成果:

  • 拟议的组合方法与13个监督,7个无监督和4个单细胞特异的GRN识别算法相比显示出更高的性能.
  • 使用接受器运行特征曲线下的面积 (AUC-ROC),精度回忆曲线下的面积 (AUC-PR) 和F1分数来评估性能.
  • 整体方法在跨不同数据集的GRN识别中实现了更高的准确性.

结论:

  • 开发的集体方法在GRN识别准确度方面取得了重大进展.
  • 这种改进的准确性有助于更深入地了解基因调节机制及其对疾病的影响.
  • 这项研究突出了集体学习和进化算法的计算生物学中的潜力.