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相关概念视频

Transcription Factors02:16

Transcription Factors

75.7K
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...
4.2K
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

5.2K
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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Master Transcription Regulators02:23

Master Transcription Regulators

6.9K
Master transcription regulators are regulatory proteins that are predominantly responsible for regulating the expression of multiple genes. Often these genes work in concert to drive a  complex process. Activation of a master transcription regulator can lead to a cascade of transcriptional activation necessary for that outcome. These regulators can directly bind to the regulatory sequences of the various genes involved, or they can indirectly regulate transcription by binding to regulatory...
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RNA Polymerase II Accessory Proteins02:36

RNA Polymerase II Accessory Proteins

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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

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High Sensitivity Measurement of Transcription Factor-DNA Binding Affinities by Competitive Titration Using Fluorescence Microscopy
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MLSNet:一种用于预测转录因子结合位点的深度学习模型.

Yuchuan Zhang1, Zhikang Wang2, Fang Ge3

  • 1School of Computer Science and Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei, Nanjing 210094, China.

Briefings in bioinformatics
|October 1, 2024
PubMed
概括

我们开发了MLSNet,这是一种深度学习模型,通过整合序列和形状特征,准确预测转录因子结合位点 (TFBS). MLSNet的性能优于现有的方法,推进了基因调节研究.

关键词:
的 DNA 序列.形状 DNA 形状 DNA 的形状多大小的卷积融合融合.超级令牌注意力和Bi-LSTM转录因子的结合点是转录因子的结合点.

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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.
  • 生物信息学是一种生物信息学.

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疾病机制至关重要.
  • 目前用于TFBS预测的深度学习模型显示出有希望的结果,但有能力提高性能.

研究的目的:

  • 引入MLSNet,这是一种用于增强TFBS预测的新型深度学习架构.
  • 为了利用多尺寸卷积融合,LSTM和DNA形状特征来提高精度.

主要方法:

  • 开发了MLSNet,将多尺寸卷积融合与LSTM网络集成在一起.
  • 整合了超级代币注意力和Bi-LSTM来提取更高阶的DNA形状特征.
  • 根据最先进的算法对165个ChIP-seq数据集进行了MLSNet的验证.

主要成果:

  • 在多个指标 (ACC,AUROC,AUPRC) 中,MLSNet在TFBS预测方面表现出卓越的表现.
  • 实现了0.8306 (ACC),0.8992 (AUROC) 和0.9035 (AUPRC) 的平均指标.
  • 显著优于第二好的方法 (1.82%的ACC,1.68%的AUROC,1.54%的AUPRC).

结论:

  • 结合多大小的卷积层,LSTM和DNA形状特征,有效地提高了TFBS预测的准确性.
  • MLSNet提供了一个强大的方法来预测TFBS,在各种细胞系和转录因子中具有一致的性能.
  • 这项研究强调了先进的深度学习架构在计算生物学中的潜力.