Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Ligand Binding and Linkage00:49

Ligand Binding and Linkage

4.8K
Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence...
4.8K
Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

8.5K
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,...
8.5K
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

396
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
396
Associative Learning01:27

Associative Learning

465
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
465
Prediction Intervals01:03

Prediction Intervals

2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.3K
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

127
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
127

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Dual contrastive learning-based reconstruction for anomaly detection in attributed networks.

PloS one·2025
Same author

Temporal dynamics unleashed: Elevating variational graph attention.

Knowledge-based systems·2024
Same author

ExGenet, Integrating Design of Experiments and Response Surface Methodology for Cancer Gene Detection in Gene Regulatory Networks.

Cancer informatics·2024
Same author

DyVGRNN: DYnamic mixture Variational Graph Recurrent Neural Networks.

Neural networks : the official journal of the International Neural Network Society·2023
Same author

A Social Network Analysis Approach to Evaluate the Relationship Between the Mobility Network Metrics and the COVID-19 Outbreak.

Health services insights·2023
Same author

DGRanker: Cancer Driver Gene Detection in Human Transcriptional Regulatory Network.

Iranian journal of biotechnology·2022
Same journal

Changes in patient-sharing patterns after oncologist departures in rural and urban settings: a Medicare cohort study.

Applied network science·2026
Same journal

Tunable network properties with Hamill and Gilbert's Social Circles generator.

Applied network science·2025
Same journal

Initialisation and network effects in decentralised federated learning.

Applied network science·2025
Same journal

The association of prescriber prominence in a shared-patient physician network with their patients receipt of and transitions between risky drug combinations.

Applied network science·2025
Same journal

Accounting for contact network uncertainty in epidemic inferences with Approximate Bayesian Computation.

Applied network science·2025
Same journal

Navigation on temporal networks.

Applied network science·2025
查看所有相关文章

相关实验视频

Updated: Jul 28, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

348

BuB:在知识图上进行链接预测的构建器-增强器模型.

Mohammad Ali Soltanshahi1, Babak Teimourpour1, Hadi Zare2

  • 1Department of Information Technology, Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran.

Applied network science
|May 30, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了用于链接预测 (LP) 的BuB模型,使用歧视性微调 (DFT) 解决一对多和多对多关系中的挑战. 这种BuB模式增强了关系的建立和加强,优于现有的方法.

关键词:
在这里,BuB BuB BuB.对歧视性微调进行微调.完成知识图表的完成.链接预测链接预测关系建设者和促进者.

更多相关视频

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

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

Published on: October 13, 2023

1.7K
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

635

相关实验视频

Last Updated: Jul 28, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

348
A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

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

Published on: October 13, 2023

1.7K
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

635

科学领域:

  • 图形神经网络的神经网络
  • 机器学习 机器学习
  • 网络科学 网络科学

背景情况:

  • 链接预测 (LP) 在各种领域至关重要,但现有的模型在一对多和许多对许多关系方面扎.
  • 歧视性微调 (DFT),调整模型部件的学习率,尚未对LP进行探索.
  • 处理复杂的关系结构仍然是链接预测的一个重大挑战.

研究的目的:

  • 介绍一个新的模型,BuB,旨在有效地处理链接预测中的一对多和多对多关系.
  • 首次探索歧视性微调 (DFT) 在链接预测中的应用.
  • 增强解决方案空间并提高链接预测模型的性能.

主要方法:

  • 拟议的BuB模型由两个组成部分组成:一个关系Builder和一个关系Booster.
  • 排名函数以极坐标与第n根重新构成,以管理复杂的关系.
  • 差别微调 (DFT) 用于调整学习率,强调构建器组件.

主要成果:

  • 在链接预测中,BuB模型成功地解决了一对多和多对多的关系挑战.
  • 使用极坐标和第n根扩大了最佳解决方案空间.
  • 实验结果表明,BuB模型超过了对基准数据集的最先进方法.

结论:

  • 结合DFT和新的排名函数的BuB模型在链接预测方面取得了重大进展.
  • 该方法有效地处理复杂的关系结构,提高预测准确度.
  • 这项研究为在基于图形的机器学习任务中应用DFT开辟了新的途径.