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

Overview Of Cell Separation And Isolation01:20

Overview Of Cell Separation And Isolation

8.1K
Cell separation was first achieved in 1964 by S. H. Seal, who separated large tumor cells from the smaller blood cells using filtration. Two years later, Pohl and Hawk performed experiments on how cells respond differently to a nonuniform electric field based on the cell type. Such observations were the inception of cell separation methods, which allow isolating a single cell type from a heterogeneous sample.
8.1K

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

Transcriptomic analysis of tissue-resident memory T cells of the fallopian tube reveals a precursor immune surveillance network for ovarian cancer prevention.

Nature communications·2026
Same author

MIND: multimodal integration with neighbourhood-aware distributions.

Nature communications·2026
Same author

Investigating the effect of climate and air pollution on prescription uptake in the England.

BMC public health·2026
Same author

Understanding the spatial determinants of the Oxford Classic prognostic signature for high-grade serous ovarian cancer.

Communications medicine·2026
Same author

Imputation Free Deep Survival Prediction with Conditional Variational Autoencoders.

Journal of healthcare informatics research·2026
Same author

SurvivEHR: a competing risks, time-to-event foundation model for multiple long-term conditions from primary care electronic health records.

NPJ digital medicine·2026

相关实验视频

Updated: Mar 14, 2026

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

2.0K

解开生物复杂性:一种深度学习方法,在单细胞数据中分离多个信号.

Christopher Yau1

  • 1Nuffield Department for Women's & Reproductive Health, University of Oxford, Oxford, UK; Health Data Research UK, London, UK.

Cell genomics
|March 12, 2026
PubMed
概括

陈和其他人. 开发了CellUntangler,这是一个深度学习模型,用于分析单细胞RNA测序 (scRNA-seq) 数据. 这个工具有效地捕获和过复杂的scRNA-seq信息中的多个生物信号.

科学领域:

  • 基因组学就是基因组学.
  • 计算生物学 计算生物学
  • 分子生物学分子生物学

背景情况:

  • 单细胞RNA测序 (scRNA-seq) 提供了细胞转录状态的快照.
  • 这些状态是由许多同时发生的细胞过程引起的.
  • 分析复杂的scRNA-seq数据来解开这些信号是具有挑战性的.

研究的目的:

  • 开发一种用于分析scRNA-seq数据的新型计算工具.
  • 为了使scRNA-seq数据集中的多个生物信号能够被捕获和过.
  • 为了改善细胞转录状态的解释.

主要方法:

  • 开发CellUntangler,这是一个基于深度学习的模型.
  • 该模型应用于scRNA-seq数据.
  • 利用机器学习在转录学中进行信号处理.

主要成果:

  • CellUntangler成功地从scRNA-seq数据中捕获了多个生物信号.
  • 该模型有效地过和区分不同的细胞过程.
  • 证明了解构复杂的转录景观的能力.

更多相关视频

Transcriptome Analysis of Single Cells
07:27

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

30.7K
Reusable Single Cell for Iterative Epigenomic Analyses
10:28

Reusable Single Cell for Iterative Epigenomic Analyses

Published on: February 11, 2022

1.7K

相关实验视频

Last Updated: Mar 14, 2026

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

2.0K
Transcriptome Analysis of Single Cells
07:27

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

30.7K
Reusable Single Cell for Iterative Epigenomic Analyses
10:28

Reusable Single Cell for Iterative Epigenomic Analyses

Published on: February 11, 2022

1.7K

结论:

  • 在scRNA-seq数据分析中,CellUntangler代表了显著的进步.
  • 该工具有助于对细胞异质性的更细致的理解.
  • 深度学习为复杂的生物数据解释提供了强大的解决方案.