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 Dynamics in Living Cells01:19

Protein Dynamics in Living Cells

2.1K
Different fluorescence-based techniques are used to study the protein dynamics in living cells. These techniques include FRAP, FRET, and PET.
Fluorescent recovery after photobleaching (FRAP) is a fluorescent-protein-based detection technique used to quantify protein movement rates within the cell. This method exposes a small portion of the cell to an intense laser beam. The laser beam causes permanent photobleaching of the fluorophore-tagged proteins in the exposed region. As the bleached...
2.1K

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

Elucidating the Urothelial-Dependent and -Independent Mechanisms Involved in the Mouse Bladder Contractility Alterations by Acute Methylglyoxal Exposure.

Biomedicines·2026
Same author

Two Faces of Cardiovascular Actions of Testosterone Dependent on the Presence or Absence of Nitric Oxide Synthases in Mice.

Circulation journal : official journal of the Japanese Circulation Society·2026
Same author

Literature-derived, context-aware gene regulatory networks improve biological predictions and mathematical modeling.

Bioinformatics (Oxford, England)·2026
Same author

6-Nitrodopamine Release From Mouse Seminal Vesicles Is Dependent on Endothelial Nitric Oxide Synthase (eNOS) Activation.

Pharmacology research & perspectives·2025
Same author

Decreased non-neurogenic acetylcholine in bone marrow triggers age-related defective stem/progenitor cell homing.

Nature communications·2025
Same author

Nihon yakurigaku zasshi. Folia pharmacologica Japonica·2025

相关实验视频

Updated: Jun 10, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

975

DynProfiler:一个Python包,用于通过深度学习技术利用信号动态的全面分析和解释.

Masato Tsutsui1,2, Mariko Okada1

  • 1Institute for Protein Research, Osaka University, Suita 565-0871, Japan.

Bioinformatics advances
|October 11, 2024
PubMed
概括

DynProfiler使用深度学习来分析疾病生物标志物的生物信号动态. 该工具从模拟中提取特征,以预测患者死亡风险并确定关键的生物途径.

更多相关视频

Time-lapse Live Imaging and Quantification of Fast Dendritic Branch Dynamics in Developing Drosophila Neurons
08:23

Time-lapse Live Imaging and Quantification of Fast Dendritic Branch Dynamics in Developing Drosophila Neurons

Published on: September 25, 2019

6.2K
Author Spotlight: Generating Neuronal Phenotypic Profiles - A Protocol to Culture and Image Human Midbrain Dopaminergic Neurons
09:21

Author Spotlight: Generating Neuronal Phenotypic Profiles - A Protocol to Culture and Image Human Midbrain Dopaminergic Neurons

Published on: July 7, 2023

1.4K

相关实验视频

Last Updated: Jun 10, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

975
Time-lapse Live Imaging and Quantification of Fast Dendritic Branch Dynamics in Developing Drosophila Neurons
08:23

Time-lapse Live Imaging and Quantification of Fast Dendritic Branch Dynamics in Developing Drosophila Neurons

Published on: September 25, 2019

6.2K
Author Spotlight: Generating Neuronal Phenotypic Profiles - A Protocol to Culture and Image Human Midbrain Dopaminergic Neurons
09:21

Author Spotlight: Generating Neuronal Phenotypic Profiles - A Protocol to Culture and Image Human Midbrain Dopaminergic Neurons

Published on: July 7, 2023

1.4K

科学领域:

  • 计算生物学 计算生物学
  • 系统生物学 系统生物学
  • 人工智能在医学中的应用

背景情况:

  • 生物信号的动态对于理解疾病机制至关重要.
  • 模拟的信号动态正在成为潜在的生物标志物.
  • 分析这些动态的传统方法通常需要手动选择特征.

研究的目的:

  • 开发一个基于深度学习的工具,DynProfiler,用于从生物信号动态中提取信息特征,而无需手动选择特征.
  • 为了将可解释的AI纳入定量,时间依赖的动态重要性得分.
  • 为了证明DynProfiler在预测死亡风险和识别乳腺癌中的生物标志物的实用性.

主要方法:

  • 利用深度学习技术来处理整个信号动态,包括中间变量,作为输入.
  • 采用可解释的人工智能解决方案,提供依赖时间的特征重要性得分.
  • 应用DynProfiler模拟乳腺癌信号动态数据.

主要成果:

  • DynProfiler成功地从模拟的乳腺癌动态中提取了高质量的特征.
  • 提取的特征在预测死亡风险方面有效.
  • 鉴定出高调节的化GSK3β作为预后不佳的重要生物标志物.

结论:

  • DynProfiler提供了一种无标签的深度学习方法,用于分析生物信号动态.
  • 该工具为临床应用提供了宝贵的见解,包括患者分层和生存预测.
  • DynProfiler有助于阐明复杂的生物系统动态,并识别新的生物标志物.