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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...
12.4K
Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

8.1K
Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
8.1K
Drug Metabolism: Phase I Reactions01:17

Drug Metabolism: Phase I Reactions

2.9K
A phase I reaction is a biochemical process that introduces a functionally reactive polar group to a substance. This transformation predominantly occurs in the liver, facilitated by the cytochrome P450 system of hemoproteins situated in the lipophilic endoplasmic reticulum of cells. The metabolite generated through this process can have varying polarities. If it is sufficiently polar, it can be easily excreted in the urine due to its water compatibility. However, if the metabolite is nonpolar,...
2.9K
Interactions Between Signaling Pathways01:19

Interactions Between Signaling Pathways

6.2K
Signaling cascades usually lack linearity. Multiple pathways interact and regulate one another, allowing cells to integrate and respond to diverse environmental stimuli.
Convergence and divergence, and cross-talk between signaling pathways
Two distinct signaling pathways can converge on a single functional unit, which may either be a single protein or a complex of proteins. The response is either functionally distinct or synergistic between the two pathways but different from the response...
6.2K
Ligand Binding Sites02:40

Ligand Binding Sites

12.6K
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...
12.6K

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相关实验视频

Updated: May 20, 2025

A Web Tool for Generating High Quality Machine-readable Biological Pathways
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A Web Tool for Generating High Quality Machine-readable Biological Pathways

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预测反应体知识库中注释的化合物的途径参与.

Erik D Huckvale1, Hunter N B Moseley1,2,3,4,5

  • 1Markey Cancer Center, University of Kentucky, Lexington, KY 40536, USA.

Metabolites
|March 26, 2025
PubMed
概括

预测未注释的生物分子的代谢途径可以改善代谢学分析. 使用Reactome知识库用于机器学习模型,与以前的方法相比,大大提高了路径预测的准确性.

关键词:
二元分类是二元分类中的一种.生物化学 生物化学机器学习是机器学习.代谢物的代谢物.多层感知器多层感知器路径路径路径路径路径原子反应器的反应监督学习学习监督学习

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相关实验视频

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科学领域:

  • 代谢学 代谢学 代谢学
  • 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:

  • 小生物分子的路径注释对于解释代谢学数据至关重要.
  • 有限的路径注释阻碍了全面的分析和omics集成.
  • 以前的预测模型仅依赖于基因和基因组的京都百科全书 (KEGG) 数据集.

研究的目的:

  • 开发和评估机器学习模型,用于预测未注释的生物分子的途径参与.
  • 探索Reactome知识库对于路径预测的实用性.
  • 改进代谢学数据解释和奥米学数据集成.

主要方法:

  • 从Reactome知识库化合物构建了一个机器学习数据集.
  • 工程化代谢物 - 途径配对特征向量.
  • 训练并评估了一个多层感知子二进制分类器.

主要成果:

  • 在Reactome数据上训练的模型比基于KEGG的模型 (0.847) 实现了0.916的更高的平均马修相关系数 (MCC).
  • Reactome数据集包含的路径数量 (3985) 比KEGG数据集 (502) 的路径数量要多.
  • 开发的模型证明了使用Reactome数据有效预测路径参与.

结论:

  • Reactome知识库是开发准确路径预测模型的宝贵资源.
  • 使用Reactome显著扩大了可预测的人类定义路径的数量.
  • 这种方法增强了改善代谢学数据分析和OMIC集成的潜力.