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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
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

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相关实验视频

Updated: Jun 9, 2025

A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions
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A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions

Published on: July 18, 2013

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使用深度学习和蛋白质序列来进行宿主-病原体相互作用的预测方法.

Taha Shakibania1, Masoud Arabfard2, Ali Najafi1

  • 1Molecular Biology Research Center, Biomedicine Technologies Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran.

Virusdisease
|October 28, 2024
PubMed
概括
此摘要是机器生成的。

预测宿主-病原体相互作用 (HPI) 对于开发新疗法至关重要. 这项研究使用深度学习和蛋白质序列来准确预测HPI,提供了一个强大的计算框架.

关键词:
卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.主体-病原体蛋白质-蛋白质相互作用消极体是一个负原子.monoMonoKGapap的使用方法

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High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

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相关实验视频

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Author Spotlight: Advanced Enteroid Model for Studying Host-Pathogen Interactions
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科学领域:

  • 计算生物学是一种计算生物学.
  • 生物信息学是一种生物信息学.
  • 传染病研究传染病研究.

背景情况:

  • 宿主-病原体相互作用 (HPI) 对了解感染至关重要.
  • 实验性HPI预测方法昂贵且耗时.
  • 计算方法为HPI预测提供了有效的替代方案.

研究的目的:

  • 开发一种基于深度学习的计算方法,用于使用蛋白序列预测HPI.
  • 评估拟议方法的准确性和可靠性.

主要方法:

  • 利用深度学习模型进行HPI预测.
  • 使用 monoMonoKGap (mMKGap) 算法 (K=2) 来从蛋白质序列中提取特征.
  • 使用Negatome数据库生成负面相互作用.
  • 在使用十倍交叉验证对三个平衡的人类病原体数据集进行了验证.

主要成果:

  • 实现了高预测准确率:99.65%,99.52%和99.66% (平均准确率为99.61%).
  • 与随机森林 (RF),支持矢量机器 (SVM) 和卷积神经网络 (CNN) 相比,表现出更高的性能.
  • 展示了mMKGap特征提取方法对二组合的有效性.

结论:

  • 提出的深度学习方法对于预测HPI具有高度准确性,稳定性和实用性.
  • 该框架为推进HPI研究和打击传染病提供了可靠的计算工具.