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

Chemotaxis and Direction of Cell Migration01:21

Chemotaxis and Direction of Cell Migration

Cells can detect chemical cues in their environment and reorganize the cytoskeleton to migrate toward them or away from them. This directional migration, called chemotaxis, is essential during embryogenesis and development, immune response, tissue repair and regeneration, and reproduction. These chemical cues can either attract or repel the cell's movement. For example, axon development is determined by a combination of chemoattractants and chemorepellents that direct the growing axon towards...

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

Compaction of chromatin domains regulates target search times of proteins.

PLoS computational biology·2026
Same author

The Role of Lamins in Genome Organisation: A Modelling Perspective.

Sub-cellular biochemistry·2025
Same author

Liquid-liquid phase separation of lamin drives altered chromatin organization in cardiomyopathic mutations of lamin A.

Nucleic acids research·2025
Same author

Genome language modeling (GLM): a beginner's cheat sheet.

Biology methods & protocols·2025
Same author

Nature of barriers determines first passage times in heterogeneous media.

Soft matter·2024
Same author

Theoretical analysis of cargo transport by catch bonded motors in optical trapping assays.

Soft matter·2023

相关实验视频

Updated: Jun 30, 2026

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
07:13

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

972

导航多元宇宙:一个车者的指南,选择多式联络生物医学数据的协调方法.

Murali Aadhitya Magateshvaren Saras1,2,3, Mithun K Mitra2, Sonika Tyagi3,4

  • 1IITB-Monash Research Academy, Mumbai, Maharashtra 400076, India.

Biology methods & protocols
|May 1, 2025
PubMed
概括

本研究介绍了使用机器学习 (ML) 进行多式联络数据分析的综合分类和指南. 它详细介绍了数据表示和整合的方法,帮助研究人员推进个性化医疗.

关键词:
数据整合数据集成.深度学习是一种深度学习.数字健康数字健康功能表示的特征表示.多式联运集成多式联运集成

更多相关视频

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.0K
Multimodal Study of Murine Cardiovascular Remodeling: Four-Dimensional Ultrasound and Mass Spectrometry Imaging
09:43

Multimodal Study of Murine Cardiovascular Remodeling: Four-Dimensional Ultrasound and Mass Spectrometry Imaging

Published on: January 10, 2025

554

相关实验视频

Last Updated: Jun 30, 2026

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
07:13

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

972
Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.0K
Multimodal Study of Murine Cardiovascular Remodeling: Four-Dimensional Ultrasound and Mass Spectrometry Imaging
09:43

Multimodal Study of Murine Cardiovascular Remodeling: Four-Dimensional Ultrasound and Mass Spectrometry Imaging

Published on: January 10, 2025

554

科学领域:

  • 生物医学信息学 生物医学信息学
  • 计算生物学 计算生物学
  • 机器学习 机器学习

背景情况:

  • 机器学习 (ML) 通过预测建模显著提高生物系统的理解.
  • 多模式数据分析,整合多种数据类型,显示出高于单模式方法的性能.
  • 缺乏ML多式联络架构的全面分类学,阻碍了有效的应用.

研究的目的:

  • 开发一个强大的框架和分类系统,用于多式联机学习分析.
  • 为了对多式联网数据的ML架构进行分类,详细说明优缺点.
  • 为选择和实施多式联运分析工作流提供实用指南.

主要方法:

  • 多式联运数据的协调是作为一个双重过程:表示和整合.
  • 一种分类学将各种表示和整合方法分为六类.
  • 为实现多式联络工作流提供了10步指南流程图.

主要成果:

  • 该研究提供了多式联运数据协调方法的详细分类.
  • 阐明了每个方法的优点和缺点.
  • 一个实用的,逐步指南有助于采用多式联运方式.

结论:

  • 这项工作为导航复杂的生物医学和临床数据分析提供了必要的指导.
  • 开发的框架支持多式联运数据集成的知情决策.
  • 这是通过先进的数据分析来推进个性化医疗的关键一步.