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相关概念视频

lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

8.4K
In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
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Protein Networks02:26

Protein Networks

3.9K
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,...
3.9K
Protein-protein Interfaces02:04

Protein-protein Interfaces

12.4K
Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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RNA-seq03:21

RNA-seq

9.7K
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...
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Conserved Binding Sites01:49

Conserved Binding Sites

4.1K
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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相关实验视频

Updated: May 8, 2025

Identification of RNAs Engaged in Direct RNA-RNA Interaction with a Long Non-Coding RNA
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Identification of RNAs Engaged in Direct RNA-RNA Interaction with a Long Non-Coding RNA

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使用混合深度学习模型预测 lncRNA - 蛋白相互作用,并使用二核酸-codon 融合特征编码.

Li Tan1, Li Mengshan2,3, Fu Yu1,4

  • 1College of Physics and Electronic Information, Gannan Normal University, Ganzhou, 341000, Jiangxi, China.

BMC genomics
|December 28, 2024
PubMed
概括

本研究介绍了LPI-DNCFF,这是一种深度学习模型,可以使用新型序列编码准确预测长非编码RNA-蛋白相互作用 (LPIs). 该模型增强了对lncRNA功能和疾病机制的理解.

关键词:
生物序列可视化可视化深度学习是一种深度学习.LncRNA与蛋白质的相互作用

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科学领域:

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

背景情况:

  • 长非编码RNAs (lncRNAs) 在生物过程和人类疾病中至关重要.
  • 识别 lncRNA-蛋白相互作用 (LPIs) 是了解 lncRNA 功能和疾病机制的关键.
  • 现有的基于序列的LPI预测方法在特征提取和集成方面面临挑战.

研究的目的:

  • 开发一个有效的深度学习模型来预测LPIs.
  • 为 lncRNA 和蛋白质提出新的序列编码方法.
  • 提高LPI预测的准确性和效率.

主要方法:

  • 开发了双核体视觉融合特征 (DNVFF) 和体融合特征 (CFF) 编码的双核体融合特征编码 (DNCFF).
  • 构建了一个深度学习模型,LPI-DNCFF,整合了全球,地方和结构特征.
  • 利用BiLSTM和注意层来捕捉依赖性和关键特征.

主要成果:

  • 在RPI1847和ATH948数据集上,LPI-DNCFF在预测LPIs方面表现出很高的准确性.
  • 达到MCC值约为97.84%和84.58%,超过了最先进的方法.
  • 对于特征提取,DNCFF编码被证明比一次性编码更有效和更彻底.

结论:

  • LPI-DNCFF是一个有效的LPI预测模型.
  • BiLSTM和注意力机制通过学习长期依赖和识别关键特征来提高模型性能.
  • 拟议的DNCFF编码方法显著改善了用于LPI预测的特征提取.