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

RNA-seq03:21

RNA-seq

9.7K
RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
9.7K

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

TransKla: A Local-Global Cross-Attention Based Transformer Approach for Prediction of Lysine Lactylation Sites.

Journal of chemical information and modeling·2026
Same author

Understanding the chemistry of re-emerging proton batteries.

Chemical Society reviews·2026
Same author

Association between questionnaire-based and accelerometer-based physical activity and the incidence of psychiatric disorders in people with loneliness or social isolation: findings from the UK Biobank.

European archives of psychiatry and clinical neuroscience·2026
Same author

Reassessing host selection in Cl-rich argyrodite electrolytes: the stability-conductivity trade-off under industrial dry-room conditions.

Chemical communications (Cambridge, England)·2026
Same author

Improving diffuse optical tomography reconstruction using an attention-based U-Net post-processing framework.

Journal of the Optical Society of America. A, Optics, image science, and vision·2026
Same author

Neural stem cell transplantation in rodent models of traumatic brain injury: a systematic review and meta-analysis.

Frontiers in bioengineering and biotechnology·2026
Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026
Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026
Same journal

EDEL: Enhancing Dense Retrievers for Curation of Biomedical Knowledge Bases.

Bioinformatics (Oxford, England)·2026
Same journal

Informative Relational Learning for Adverse Reaction Prediction with Enhanced Generalization to Novel Drugs.

Bioinformatics (Oxford, England)·2026
Same journal

An interpretable deep learning framework uncovers features governing CRISPR-Cas9 genome-editing efficiency.

Bioinformatics (Oxford, England)·2026
Same journal

3DICE: Interpretable 3D Cross-Modal Learning for Drug-Target Interaction Prediction and Large-Scale Drug Discovery.

Bioinformatics (Oxford, England)·2026
查看所有相关文章

相关实验视频

Updated: May 20, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
09:19

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

4.8K

m2ST:用于空间分辨率的转录组学的双重多尺度图集群.

Wei Zhang1,2, Ziqi Zhang2, Hailong Yang2

  • 1The School of Artificial Intelligence and Computer Science, Nantong University, Nantong, 226019, China.

Bioinformatics (Oxford, England)
|April 24, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了m2ST,这是一种用于空间转录学的双重多尺度图集群方法. m2ST通过在多个尺度上分析数据来增强空间域注释,优于现有方法.

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

616
ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
05:12

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

11.3K

相关实验视频

Last Updated: May 20, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
09:19

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

4.8K
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

616
ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
05:12

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

11.3K

科学领域:

  • 计算生物学 计算生物学
  • 生物信息学是一种生物信息学.
  • 基因组学就是基因组学.

背景情况:

  • 空间聚类对于分析空间转录组学数据至关重要.
  • 当前的图形神经网络方法在单个尺度分析方面扎,限制了特征的区分能力.
  • 需要定制的集群方法来提高空间域注释的准确性.

研究的目的:

  • 提出m2ST,一个新的空间转录学双重多尺度图集群方法.
  • 解决单个尺度分析的局限性,并改进空间域注释.
  • 增强提取特征的区分能力,以进行聚类.

主要方法:

  • m2ST使用多尺度掩盖图形自编码器进行多尺度表示提取.
  • 随机掩盖机制和缩放的等号误差用于知识蒸.
  • 一个定制的多尺度集群框架集成了尺度通用和尺度特定的信息,使用香农来进行动态尺度调整.

主要成果:

  • m2ST从空间转录组数据中提取多尺度表示.
  • 该方法通过整合规模特定和规模通用的信息,实现了强大的注释性能.
  • 广泛的实验表明m2ST在多个数据集上优于现有方法.

结论:

  • m2ST为转录学中的空间域聚类提供了一种优越的方法.
  • 双重多尺度策略有效地捕捉了不同分辨率的生物见解.
  • 这种方法通过空间转录学来推进复杂生物机制的分析.