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

9.8K
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...
9.8K
DNA Microarrays02:34

DNA Microarrays

17.1K
Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
17.1K

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

An Explainable Deep Learning Framework Integrating DNA Sequence and Transcription Initiation Signals for Gene Expression Prediction.

ACS synthetic biology·2026
Same author

LysePred: A Multiscale Convolutional Neural Network for Predicting Hemolytic Activity of Antimicrobial Peptides.

ACS synthetic biology·2026
Same author

SpliceSelectNet: a hierarchical Transformer-based deep learning model for splice site prediction.

Nucleic acids research·2026
Same author

An Interpretable Deep Learning Framework Leveraging RNA Foundation Model and Capsule Networks for Accurate Prediction of RNA 2'-O-Methylation Sites.

Journal of chemical information and modeling·2026
Same author

MPMFMol: Multitask Self-Supervised Pretraining with Multimodal Fine-Tuning for Molecular Property Prediction.

Journal of chemical information and modeling·2026
Same author

Quantum computing applications in drug discovery.

Briefings in bioinformatics·2026
Same journal

EC-isHCR: A rapid method for in situ hybridization chain reaction in diverse animal samples.

Methods (San Diego, Calif.)·2026
Same journal

Single-Molecule methods to investigate mechanisms of transcription by RNA polymerase of Mycobacterium tuberculosis.

Methods (San Diego, Calif.)·2026
Same journal

Detection and sequencing of Usutu virus during mosquito surveillance: Use of multiple assays and techniques for identification at low levels.

Methods (San Diego, Calif.)·2026
Same journal

Experimental validation of an AI-driven digital healthcare platform for oral health behavior and plaque assessment among vietnamese children.

Methods (San Diego, Calif.)·2026
Same journal

Zeta potential: An efficient and cost-effective alternative for investigating cell-surface interactions.

Methods (San Diego, Calif.)·2026
Same journal

An automated workflow for quantifying the formation of synuclein aggregates in human dopaminergic neurons.

Methods (San Diego, Calif.)·2026
查看所有相关文章

相关实验视频

Updated: May 23, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
10:12

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

18.4K

OmniClust:用于单细胞和空间转录组学数据的多功能集群工具包.

Yaxuan Cui1, Yang Cui2, Yi Ding2

  • 1Department of Computer Science, University of Tsukuba, Tsukuba 3058577, Japan.

Methods (San Diego, Calif.)
|March 8, 2025
PubMed
概括
此摘要是机器生成的。

OmniClust是一个新的工具包,集成了单细胞RNA测序 (scRNA-seq) 和空间转录组学数据. 该工具使用深度和机器学习来准确地对转录组数据进行聚类和生物解释.

关键词:
乳腺癌是什么? 乳腺癌是什么深度学习是一种深度学习.RNA转录基因组测序的RNA转录基因组测序空间转录组学 空间转录组学这就是 scRNA-seqq.

更多相关视频

Author Spotlight: Deciphering the Cellular Mysteries of Intermuscular Adipose Tissue in Humans
05:59

Author Spotlight: Deciphering the Cellular Mysteries of Intermuscular Adipose Tissue in Humans

Published on: May 3, 2024

575
Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore
06:01

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Published on: December 12, 2019

8.4K

相关实验视频

Last Updated: May 23, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
10:12

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

18.4K
Author Spotlight: Deciphering the Cellular Mysteries of Intermuscular Adipose Tissue in Humans
05:59

Author Spotlight: Deciphering the Cellular Mysteries of Intermuscular Adipose Tissue in Humans

Published on: May 3, 2024

575
Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore
06:01

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Published on: December 12, 2019

8.4K

科学领域:

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

背景情况:

  • 像单细胞RNA测序 (scRNA-seq) 和空间转录组学这样的RNA转录组测序技术正在迅速发展.
  • 这些独特但相关的技术需要集成的算法工具包进行全面分析.
  • 现有的工具往往只专注于一种技术,因此需要统一的方法.

研究的目的:

  • 开发OmniClust,这是一个集成的算法工具包,用于分析scRNA-seq和空间转录学数据.
  • 利用深度学习和机器学习来实现强大的功能学习和集群.
  • 为了证明OmniClust在从复杂的转录组数据中发现生物学洞察力的实用性.

主要方法:

  • OmniClust使用深度学习算法来进行特征学习和空间转录组数据的聚类.
  • 机器学习算法用于聚类scRNA-seq数据.
  • 该工具包在两种技术的多个基准数据集上进行了严格的测试.

主要成果:

  • OmniClust在12个空间转录组学基准数据集中展示了高集群精度.
  • 该工具包在四个scRNA-seq基准数据集上实现了高集群精度.
  • 对乳腺癌数据的应用揭示了在转录组中发现生物学意义的潜力.

结论:

  • OmniClust是一个有效的集群工具,用于单细胞和空间转录组学数据.
  • 该工具包在分析各种转录组数据集方面表现出卓越的性能.
  • OmniClust促进了对癌症转录组数据的更深入的生物学解释.