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相关概念视频

Protein-protein Interfaces02:04

Protein-protein Interfaces

12.5K
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...
12.5K
lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

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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
Ligand Binding Sites02:40

Ligand Binding Sites

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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
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相关实验视频

Updated: Jul 1, 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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通过使用多个特征和随机森林算法深度学习框架预测lncRNA-蛋白相互作用.

Ying Liang1, XingRui Yin1, YangSen Zhang1

  • 1College of Computer and Information Engineering, Jiangxi Agricultural University, Zhimin Avenue, Nanchang, China.

BMC bioinformatics
|March 13, 2024
PubMed
概括

本研究介绍了LPI-MFF,这是一种用于预测RNA-蛋白相互作用 (RPI) 的新型计算模型. 通过整合多个数据源,LPI-MFF提高了预测准确性和通用性,优于现有方法.

关键词:
功能 聚变的特点 聚变的特点LncRNA蛋白相互作用多种功能多种功能.随机森林算法 随机森林算法

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相关实验视频

Last Updated: Jul 1, 2025

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RNA Pull-down Procedure to Identify RNA Targets of a Long Non-coding RNA
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科学领域:

  • 分子生物学分子生物学
  • 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:

  • RNA-蛋白相互作用 (RPI) 是许多生物过程的基础.
  • 实验性识别RPI是耗时和昂贵的.
  • 目前用于RPI预测的计算方法缺乏足够的稳定性和通用性.

研究的目的:

  • 开发一个先进的计算模型,LPI-MFF,用于准确的RPI预测.
  • 提高RPI预测模型的稳定性和通用性.
  • 解决现有的机器学习和基于深度学习的RPI预测方法的局限性.

主要方法:

  • LPI-MFF集成了包括蛋白质与蛋白质相互作用,序列特征,二次结构和物理化学性质在内的多来源信息.
  • 使用随机森林算法进行有效的特征选.
  • 一个卷积神经网络 (CNN) 作为最终的分类模型.

主要成果:

  • 通过五重交叉验证,LPI-MFF在RPI1807上实现了97.60%的高准确率,在NPInter数据集上达到97.67%.
  • 该模型在独立测试组 (RPI1168) 上表现出强的性能,准确率为84.9%.
  • 在Mus musculus数据集上记录了90.91%的准确性.

结论:

  • 与普遍的RPI预测方法相比,LPI-MFF表现出优越的稳定性和概括能力.
  • 多源信息融合策略有效地提高了RPI预测性能.
  • 这个模型为推进RPI研究提供了一个有前途的计算工具.