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相关概念视频

Cis-regulatory Sequences02:02

Cis-regulatory Sequences

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...
DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
RNA-seq03:21

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...
Cis-regulatory Sequences02:02

Cis-regulatory Sequences

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: Jun 28, 2026

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
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深度图像:从scRNA-seq数据中深入分析基因调控网络

Xiguo Zhou1, Jingyi Pan1, Liang Chen1

  • 1College of Computer and Information Engineering, Tianjin Normal University, Tianjin 300387, China.

Biomolecules
|July 27, 2024
PubMed
概括
此摘要是机器生成的。

DeepIMAGER是一种新的深度学习工具,可以从单细胞RNA测序数据中准确推断细胞特异基因调节网络 (GRNs). 与现有方法相比,它提高了精度,减少了虚假阳性.

关键词:
细胞类型 细胞类型深度学习是一种深度学习.基因监管网络 基因监管网络这就是scRNA-seqq.

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相关实验视频

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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.
  • 系统生物学 系统生物学

背景情况:

  • 基因调节网络 (GRNs) 对细胞过程至关重要,但推断细胞特异性的GRNs是具有挑战性的.
  • 使用RNA测序数据的现有计算方法往往产生低精度和高假阳性.

研究的目的:

  • 开发一个先进的计算工具,DeepIMAGER,用于准确推断细胞特异性的GRNs.
  • 利用深度学习和数据集成来改进GRN推断.

主要方法:

  • DeepIMAGER使用监督深度学习方法,将基因对的共同表达转化为类似图像的数据.
  • 该模型以单细胞RNA测序和ChIP测序数据进行训练,并包含转录因子结合信息.
  • 它与六个细胞系的十个流行的GRN推断工具进行了验证.

主要成果:

  • 与现有工具相比,DeepIMAGER表现出优越的性能和对数据丢失的稳定性.
  • 对多发性骨髓瘤数据集的应用确定了树突细胞中关键转录因子的潜在GRN.
  • 该工具从单细胞RNA测序数据中准确地解码GRN.

结论:

  • 在推断细胞特异性GRN方面,DeepIMAGER代表了重大进展.
  • 这种工具提供了高精度和稳定性,使其对研究复杂的监管网络非常有价值.
  • 深度IMAGER有助于更深入地了解不同类型细胞中的分子机制.