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相关概念视频

Transcription Factors02:16

Transcription Factors

75.8K
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...
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Conserved Binding Sites01:49

Conserved Binding Sites

4.2K
Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
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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...
6.4K
General Transcription Factors01:30

General Transcription Factors

5.3K
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...
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Eukaryotic Transcription Activators02:42

Eukaryotic Transcription Activators

11.0K
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...
11.0K
RNA Polymerase II Accessory Proteins02:36

RNA Polymerase II Accessory Proteins

9.2K
Proteins that regulate transcription can do so either via direct contact with RNA Polymerase or through indirect interactions facilitated by adaptors, mediators, histone-modifying proteins, and nucleosome remodelers. Direct interactions to activate transcription is seen in bacteria as well as in some eukaryotic genes. In these cases, upstream activation sequences are adjacent to the promoters, and the activator proteins interact directly with the transcriptional machinery. For example, in...
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相关实验视频

Updated: Jun 26, 2025

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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通过深度学习预测转录因子绑定站点

Nimisha Ghosh1, Daniele Santoni2, Indrajit Saha3

  • 1Department of Computer Science and Information Technology, Institute of Technical Education and Research, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar 751030, India.

International journal of molecular sciences
|May 11, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的深度学习模型,用于预测转录因子结合部位 (TFBS). 该方法有效地预测多个细胞系的TFBS,为分子生物学提供了洞察力.

关键词:
DNA 序列的 DNA 序列.囊网络是一个囊网络.深度学习是一种深度学习.转录因子结合位点 (TFBS) 是一个

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科学领域:

  • 基因组学就是基因组学.
  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学

背景情况:

  • 准确预测转录因子结合位点 (TFBS) 对于理解基因调节和开发治疗策略至关重要.
  • 现有的机器学习方法往往缺乏嵌入遗传数据的强大方法,限制了它们的有效性.

研究的目的:

  • 开发和评估一种新的深度学习模型,用于准确的TFBS预测.
  • 为解决TFBS预测当前机器学习方法中遗传数据嵌入的局限性.

主要方法:

  • 一个基于变压器的双向编码器,与双向长短期存储器 (LSTM) 层集成.
  • 用囊层进行了转录因子结合部位的最终预测.
  • 该模型使用5个ENCODE细胞系 (A549,GM12878,Hep-G2,H1-hESC,Hela) 的基准ChIP-seq数据集进行训练和验证.

主要成果:

  • 拟议的模型在预测单个细胞系内的TFBS方面表现出很高的准确性.
  • 在跨细胞系预测方面取得了令人满意的结果,表明了可概括性.
  • 进一步的实验证实了跨细胞系的高预测准确性,使广泛的交叉转录因子分析成为可能.

结论:

  • 开发的深度学习方法为TFBS预测提供了强大而有效的方法.
  • 该模型执行跨细胞系预测的能力为分子生物学研究开辟了新的途径.
  • 这项工作为了解基因表达调节和治疗标识提供了有价值的工具.