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

Protein Networks02:26

Protein Networks

4.5K
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,...
4.5K
Protein-protein Interfaces02:04

Protein-protein Interfaces

14.4K
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...
14.4K
Extraction: Advanced Methods00:56

Extraction: Advanced Methods

1.1K
Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
1.1K

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

Disentangling shared and unique effects of parenting on psychopathology: Evidence from two prospective, genetically sensitive cohort studies.

JCPP advances·2026
Same author

Factors Influencing Perceived Risk of Lonely Death Among Older Adults Living Alone: Analysis of the 2024 Seoul Senior Citizen Survey.

International journal for quality in health care : journal of the International Society for Quality in Health Care·2026
Same author

Predictive value of glycosylated hemoglobin levels for large for gestational age infants in women with pregestational diabetes according to body mass index.

Obstetrics & gynecology science·2026
Same author

A Novel DRD2 Antagonist, SD2-2305, Exerts Anticancer Effects in Colorectal Cancer Cells through G1 Arrest and Caspase-Dependent Apoptosis.

Biomolecules & therapeutics·2026
Same author

Continuous Monitoring of Positive Airway Pressure Therapy with a Smartphone-Based Home Sleep Apnea Test.

Medicina (Kaunas, Lithuania)·2026
Same author

Current Status and Trends of Two-Dimensional Correlation Spectroscopy (2D-COS).

Applied spectroscopy·2026

相关实验视频

Updated: Jan 16, 2026

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
06:19

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Published on: August 16, 2024

827

基于CNN的可解释特征提取方法,考虑对交互.

Kyuchang Chang1, Sujin Lee2, Jun-Geol Baek3

  • 1Department of Artificial Intelligence, Jeju National University, Jeju 63243, Republic of Korea.

Sensors (Basel, Switzerland)
|September 27, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一个新的框架,通过捕捉变量相互作用来改进多变量时间序列分类. 该方法提高了性能,并提供了对单个和对对变量效应的可解释的见解.

关键词:
卷积神经网络 (CNN) 是一种神经网络.可解释的人工智能 (XAI)功能提取 特性提取相互作用效应的相互作用效应.多变量时间序列的分类.双向相互作用的对对交互.

更多相关视频

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

1.2K

相关实验视频

Last Updated: Jan 16, 2026

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
06:19

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Published on: August 16, 2024

827
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

1.2K

科学领域:

  • 机器学习 机器学习
  • 数据科学数据科学数据科学
  • 时间序列分析时间序列分析

背景情况:

  • 卷积神经网络 (CNN) 擅长进行多变量时间序列分析,但难以检测统计相互作用.
  • 现有的方法在捕捉时间序列数据中变量之间的复杂关系方面存在局限性.

研究的目的:

  • 提出一种新的框架,以提高多变量时间序列分类性能.
  • 为了能够客观地评估单个变量影响和双对相互作用效应.
  • 克服CNN在检测统计相互作用方面的结构限制.

主要方法:

  • 用于特征提取的卷积过器和层结构的创意修改.
  • 开发方法来捕捉对交互的影响.
  • 与可解释模型进行因果分析和特征重要性计算的集成.

主要成果:

  • 成功提取了在合成数据中解释对互动的特征.
  • 与基线方法相比,在现实世界多变量时间序列数据上表现出优异的分类性能.
  • 为深入的因果分析量化了单个和双向变量效应.

结论:

  • 拟议的框架为多变量时间序列分类提供了一个实用和可解释的解决方案.
  • 它在变量相互作用至关重要的领域特别有效,例如医疗保健,金融和制造业.
  • 该方法在分析具有相互作用变量的复杂时间序列数据方面取得了重大进展.