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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

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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,...
4.0K
Protein-Protein Interfaces02:04

Protein-Protein Interfaces

3.8K
3.8K
Protein Organization01:24

Protein Organization

6.5K
Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
The primary structure of a protein is its amino acid sequence....
6.5K
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
Ligand Binding Sites02:40

Ligand Binding Sites

12.8K
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 4, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

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解释蛋白质-蛋白质相互作用与基于知识图的语义相似性.

Rita T Sousa1, Sara Silva1, Catia Pesquita1

  • 1LASIGE, Faculdade de Ciências da Universidade de Lisboa, Lisboa, Portugal.

Computers in biology and medicine
|February 3, 2024
PubMed
概括

我们介绍KGsim2vec,这是一种用于生物医学研究的新型可解释的人工智能方法. 这种方法通过使用知识图的语义相似性来提高机器学习模型的解释性,改善预测并识别数据偏差.

科学领域:

  • 生物医学信息学是生物医学信息学.
  • 科学领域的人工智能
  • 机器学习用于药物发现.

背景情况:

  • 机器学习 (ML) 和人工智能 (AI) 越来越多地用于生物医学应用,如蛋白质-蛋白质相互作用预测.
  • 可解释的人工智能 (XAI) 对于科学发现至关重要,能够理解ML机制和数据偏差.
  • 知识图 (KGs) 代表域知识,但通常使用无法解释的嵌入式来探索.

研究的目的:

  • 开发一种可解释的方法,用于在生物医学应用的知识图中表示实体.
  • 提高复杂生物领域的机器学习模型的可解释性和预测性能.
  • 为无法解释的知识图嵌入提供替代方案.

主要方法:

  • 提出了KGsim2vec,这是一种创新的方法,用于生成可解释的向量表示,使用面向知识图中的面向语义相似性.
  • 利用各种机器学习模型 (决策树,遗传编程,随机森林,极端梯度提升) 来预测实体关系.
  • 在知识图中跨多个语义方面计算的相似性.

主要成果:

  • 在实体相似性表示中考虑多个语义方面,提高了可解释性和预测性能.
  • KGsim2vec的性能优于传统的黑子方法,如知识图嵌入和图神经网络.
关键词:
可解释的人工智能知识图表知识图表机器学习是机器学习.蛋白蛋白相互作用预测的预测语义上的相似性 语义上的相似性

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TurboID-Based Proximity Labeling for In Planta Identification of Protein-Protein Interaction Networks
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TurboID-Based Proximity Labeling for In Planta Identification of Protein-Protein Interaction Networks

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Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay PCA in Living Cells
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Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay PCA in Living Cells

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TurboID-Based Proximity Labeling for In Planta Identification of Protein-Protein Interaction Networks
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  • 开发的模型能够捕捉生物现象并揭示数据偏差.
  • 结论:

    • 与当前基于嵌入的方法相比,KGsim2vec为生物医学应用提供了更容易解释和更有效的方法.
    • 该方法通过提供对生物关系和数据特征的可解释的见解来增强科学发现.
    • 这项工作推动了可解释AI与知识图的整合,以进行强大的生物医学数据分析.