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相关概念视频

Protein-protein Interfaces02:04

Protein-protein Interfaces

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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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Protein Networks02:26

Protein Networks

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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,...
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Factors Affecting the Risk of Infection01:26

Factors Affecting the Risk of Infection

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The hosts' susceptibility to infection depends on several factors. The integrity of the skin and mucous membranes helps protect the body against microbial attacks. When the skin is altered, the chance of infection, limb loss, and even death increases.
The integrity and count of the white blood cells help the body resist pathogens and fight infection. When impaired, it reduces the body's resistance to pathogens. The acidic pH levels of the gastrointestinal, genitourinary tracts, and skin...
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Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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相关实验视频

Updated: May 6, 2026

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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用机器学习算法预测宿主-病原体相互作用:一个范围审查.

Rasool Sahragard1, Masoud Arabfard2, Ali Najafi1

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

Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases
|April 12, 2025
PubMed
概括
此摘要是机器生成的。

机器学习有效地预测宿主-病原体相互作用 (HPI),以树为基础的算法是最常见的. 在数据集标准化和可解释性方面仍然存在挑战,以推动在病原体研究中推进AI.

关键词:
生物信息学是一种生物信息学.宿主-病原体相互作用机器学习算法 机器学习算法预测算法预测算法蛋白质与蛋白质的相互作用

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High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions
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High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

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

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相关实验视频

Last Updated: May 6, 2026

A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions
13:56

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

Published on: July 18, 2013

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High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions
14:58

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

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

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科学领域:

  • 微生物学和免疫学
  • 计算生物学 计算生物学
  • 人工智能在医学中的应用

背景情况:

  • 病原性微生物对全球健康构成挑战,需要了解宿主-病原体相互作用 (HPI).
  • 蛋白与蛋白相互作用 (PPI) 是HPI的关键,对治疗开发至关重要.
  • 对HPI的实验方法是劳动密集型的;人工智能和机器学习提供高效的预测.

研究的目的:

  • 系统地审查和评估用于预测宿主-病原体相互作用 (HPI) 的机器学习方法.
  • 根据宿主/病原体类型,算法和评估指标对现有研究进行分类.
  • 确定挑战,并为人工智能驱动的HPI预测未来研究提供路线图.

主要方法:

  • 2019-2024年基于机器学习的HPI预测研究的范围审查.
  • 使用与HPI相关的关键字搜索了知名的数据库.
  • 根据标题和摘要评估,在46篇相关文章中选择了30篇.

主要成果:

  • 基于树的算法 (随机森林,渐变增强) 在HPI预测中最为普遍.
  • 深度学习模型 (CNN,RNN) 是有前途的,但需要大量的标记数据.
  • 在数据集标准化和模型可解释性方面存在重大差距.

结论:

  • 机器学习对HPI预测具有重大潜力.
  • 解决数据集质量,特征选择和模型透明度方面的挑战至关重要.
  • 本综述提供了计算方法的系统比较,指导未来的AI驱动病原体研究.