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相关概念视频

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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Transformers in Distribution System01:27

Transformers in Distribution System

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Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
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Types Of Transformers01:16

Types Of Transformers

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Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
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Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

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In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
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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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Predicting Reaction Outcomes

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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,...
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相关实验视频

Updated: Jul 12, 2025

A Protocol for Computer-Based Protein Structure and Function Prediction
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HNetGO:通过异质网络变压器进行蛋白质功能预测.

Xiaoshuai Zhang1, Huannan Guo2, Fan Zhang3

  • 1School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China.

Briefings in bioinformatics
|October 20, 2023
PubMed
概括

通过使用新型异质网络和预训练模型整合序列相似性和蛋白质相互作用,HNetGO增强了蛋白质功能预测. 这种方法提高了准确性,特别是对于细胞组件和分子功能的注释.

关键词:
基因本体学 基因本体学图表神经网络的神经网络异质网络是一种异质的网络.蛋白质功能 标注 标注

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科学领域:

  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学
  • 基因组学就是基因组学.

背景情况:

  • 蛋白质功能注释对于理解基因组后的分子生命至关重要.
  • 整合多源数据可以改善蛋白质功能预测,但现有的方法面临特征工程和模型整合的挑战.
  • 深度学习模型经常忽视未标记的序列数据,限制了它们的特征提取能力.

研究的目的:

  • 为了开发一个端到端的蛋白质功能注释模型,HNetGO.
  • 利用异质网络整合蛋白质序列相似性和蛋白质-蛋白质相互作用数据.
  • 利用预训练模型从蛋白质序列中提取语义特征.

主要方法:

  • HNetGO采用异质网络,将蛋白质序列相似性和蛋白质与蛋白质相互作用信息结合起来.
  • 使用预训练模型从蛋白质序列中提取语义特征.
  • 一个基于注意力的图形神经网络从异质网络中提取节点级特征,用于功能预测.

主要成果:

  • HNetGO在人类数据集上实现了最先进的性能.
  • 该模型在预测与细胞组件相关的蛋白质功能方面表现出卓越的准确性.
  • 在预测分子功能方面观察到显著的改进.

结论:

  • HNetGO提供了一种有效的端到端解决方案,用于蛋白质功能注释.
  • 不同质网络和预训练模型的整合促进了蛋白质功能预测.
  • 该模型显示了生物研究的巨大潜力,特别是在理解细胞和分子功能方面.