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

9.9K
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...
9.9K

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

A review of deep learning approaches for drug synergy prediction in cancer.

npj drug discovery·2026
Same author

Premature Aortic Stiffness in Relation to Cerebral Small Vessel Disease, Cognitive Decline, Major Cardiovascular Events and Mortality in Dialysis.

American journal of nephrology·2026
Same author

Reprint of: Effect of perioperative preemptive analgesia on hippocampal GABAA receptor α1/α5 balance in aged mild cognitive impairment rats.

Brain research bulletin·2026
Same author

Pathogenicity prediction for noncanonical splice-altering variants based on multimodal feature fusion.

Briefings in bioinformatics·2026
Same author

iDualG4: A Dual-Channel Deep Learning Framework for Predicting In Vivo G-Quadruplexes.

Biomolecules·2026
Same author

AdPrST:An Adversarial Graph Deep Learning Pre-clustering Framework for Deciphering Spatiotemporal Structures in Spatially Resolved Transcriptomics.

IEEE transactions on computational biology and bioinformatics·2026
Same journal

circ2DGNN: circRNA-Disease Association Prediction via Transformer-Based Graph Neural Network.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

Hierarchical Hypergraph Learning in Association- Weighted Heterogeneous Network for miRNA- Disease Association Identification.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

Discriminative Domain Adaption Network for Simultaneously Removing Batch Effects and Annotating Cell Types in Single-Cell RNA-Seq.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

MLW-BFECF: A Multi-Weighted Dynamic Cascade Forest Based on Bilinear Feature Extraction for Predicting the Stage of Kidney Renal Clear Cell Carcinoma on Multi-Modal Gene Data.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

An End-to-End Knowledge Graph Fused Graph Neural Network for Accurate Protein-Protein Interactions Prediction.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

Generative Biomedical Event Extraction With Constrained Decoding Strategy.

IEEE/ACM transactions on computational biology and bioinformatics·2024
查看所有相关文章

相关实验视频

Updated: Jun 25, 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

18.5K

scVSC:为单细胞转录组数据进行深度变异子空间聚类.

Zile Wang, Haiyun Wang, Jianping Zhao

    IEEE/ACM transactions on computational biology and bioinformatics
    |May 27, 2024
    PubMed
    概括
    此摘要是机器生成的。

    单细胞RNA测序 (scRNA-seq) 数据分析得到了scVSC的改进,这是一个新的深度学习算法. 这种无监督的集群工具提高了识别细胞类型和生物通路的准确性和效率.

    更多相关视频

    Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level
    09:45

    Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

    Published on: March 14, 2022

    2.9K
    Transcriptome Analysis of Single Cells
    07:27

    Transcriptome Analysis of Single Cells

    Published on: April 25, 2011

    29.9K

    相关实验视频

    Last Updated: Jun 25, 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

    18.5K
    Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level
    09:45

    Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

    Published on: March 14, 2022

    2.9K
    Transcriptome Analysis of Single Cells
    07:27

    Transcriptome Analysis of Single Cells

    Published on: April 25, 2011

    29.9K

    科学领域:

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

    背景情况:

    • 单细胞RNA测序 (scRNA-seq) 能够在单个细胞水平上进行基因表达分析,揭示细胞异质性.
    • 由于技术限制,scRNA-seq数据往往稀疏且异质,这给分析带来了挑战.

    研究的目的:

    • 引入scVSC,一个无监督的集群算法,旨在解决scRNA-seq数据的局限性.
    • 为了提高从scRNA-seq数据的细胞亚群识别的准确性和效率.

    主要方法:

    • 开发了scVSC,这是一个无监督的集群算法,利用深度表示神经网络.
    • 在子空间模型中集成变量推断,以规范隐性空间并防止过拟合.

    主要成果:

    • 与多个数据集的最先进方法相比,scVSC表现出卓越的集群精度和运行效率.
    • 该算法成功识别了差异表达的基因,并发现了关键的生物通路.
    • scVSC可视地揭示了细胞分化轨迹.

    结论:

    • scVSC为分析稀疏和异质的scRNA-seq数据提供了强大的和高效的解决方案.
    • 该方法增强了细胞异质性,基因表达模式和生物见解的发现.