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

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 stochastic agent-based model for simulating tumor-immune dynamics and evaluating therapeutic strategies.

Mathematical biosciences and engineering : MBE·2026
Same author

Multiscale modeling reveals synergy between CCL19 and PD-1 blockade in reshaping the TNBC microenvironment.

NPJ systems biology and applications·2026
Same author

Modelling infectious disease transmission dynamics in conference environments: An agent-based approach.

Mathematical biosciences and engineering : MBE·2026
Same author

Effects of Multi-Phase Control Mechanism on Fibroblast Dynamics: A Segmented Mathematical Modeling Approach.

Bulletin of mathematical biology·2026
Same author

Combination therapy for colorectal cancer with anti-PD-L1 and cancer vaccine: A multiscale mathematical model of tumor-immune interactions.

Mathematical biosciences·2026
Same author

Modeling tumor progression in heterogeneous microenvironments: A cellular automata approach.

Journal of theoretical biology·2026
Same journal

Predicting piRNA-Disease Associations Based on Dual-View Learning and Multi-head Self-Attention Mechanism Fusion.

Interdisciplinary sciences, computational life sciences·2026
Same journal

DTANet+: Dual Interaction and Kernel-Diverse Network for Drug-Target Affinity Prediction.

Interdisciplinary sciences, computational life sciences·2026
Same journal

STNMAE: Identifying Spatial Domains from Spatial Transcriptomics Data with Neighbor-Aware Multi-view Masked Graph Autoencoder.

Interdisciplinary sciences, computational life sciences·2026
Same journal

Diagnosis and Prediction of Alzheimer's Disease via a High-Level Convolutional Block Attention Module-Residual Network.

Interdisciplinary sciences, computational life sciences·2026
Same journal

Deep3D-DTA: A Tri-Modal Deep Learning Framework for Binding Affinity Prediction Leveraging 3D Structural Representations of Drugs and Targets.

Interdisciplinary sciences, computational life sciences·2026
Same journal

ST-LDAW: A Topic-Model and Damped Weighted Least-Squares Method for Integrative Deconvolution of Single-Cell and Spatial Transcriptomics.

Interdisciplinary sciences, computational life sciences·2026
查看所有相关文章

相关实验视频

Updated: May 26, 2025

Single-cell RNA Sequencing of Fluorescently Labeled Mouse Neurons Using Manual Sorting and Double In Vitro Transcription with Absolute Counts Sequencing DIVA-Seq
07:49

Single-cell RNA Sequencing of Fluorescently Labeled Mouse Neurons Using Manual Sorting and Double In Vitro Transcription with Absolute Counts Sequencing DIVA-Seq

Published on: October 26, 2018

9.4K

scRDiT:通过扩散变压器生成单细胞RNA-seq数据并加速采样.

Shengze Dong1, Zhuorui Cui1, Ding Liu2

  • 1School of Computer Science and Technology, Tiangong University, Tianjin, 300387, China.

Interdisciplinary sciences, computational life sciences
|February 21, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了scRNA-seq扩散变压器 (scRDiT),这是一种创新的生成AI方法,用于创建现实的虚拟单细胞RNA测序数据集. scRDiT有效地捕获独特的数据特征,使生物研究能够生成高质量的合成数据.

关键词:
扩散模型是一个扩散模型.神经网络的神经网络的神经网络一个单细胞RNA-seqq.变压器变压器变压器

更多相关视频

An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing
10:00

An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing

Published on: May 23, 2018

17.5K
Transcriptome Analysis of Single Cells
07:27

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

29.8K

相关实验视频

Last Updated: May 26, 2025

Single-cell RNA Sequencing of Fluorescently Labeled Mouse Neurons Using Manual Sorting and Double In Vitro Transcription with Absolute Counts Sequencing DIVA-Seq
07:49

Single-cell RNA Sequencing of Fluorescently Labeled Mouse Neurons Using Manual Sorting and Double In Vitro Transcription with Absolute Counts Sequencing DIVA-Seq

Published on: October 26, 2018

9.4K
An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing
10:00

An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing

Published on: May 23, 2018

17.5K
Transcriptome Analysis of Single Cells
07:27

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

29.8K

科学领域:

  • 计算生物学 计算生物学
  • 基因组学就是基因组学.
  • 人工智能的人工智能

背景情况:

  • 单细胞RNA测序 (scRNA-seq) 对于理解细胞异质性至关重要.
  • 现有的分析工具很难捕捉scRNA-seq数据的独特统计特性,并生成现实的虚拟数据集.

研究的目的:

  • 开发一种创造性的方法来创建高质量的虚拟scRNA-seq数据集.
  • 解决目前复制scRNA-seq数据特征的方法的局限性.

主要方法:

  • 介绍了scRNA-seq扩散变压器 (scRDiT),这是一个基于Denoising扩散概率模型 (DDPMs) 和扩散变压器 (DiTs) 的神经网络.
  • 在真实scRNA-seq数据上使用高斯噪声进行代增加和恢复步骤.
  • 集成的脱扩散隐性模型 (DDIMs) 以加快采样过程.

主要成果:

  • 在两个不同的数据集中生成虚拟scRNA-seq数据方面表现出卓越的性能.
  • 在培训期间,成功地从实际scRNA-seq样本中学习了基本数据特征.
  • 能够生成大量高质量的合成scRNA-seq样本.

结论:

  • scRDiT提供了一种统一的方法来生成高质量的虚拟scRNA-seq数据.
  • 该方法使研究人员能够在他们的特定数据集上训练模型,以进行定制合成数据生成.
  • 通过提供强大的数据增强和模拟工具,促进先进的生物研究.