Search research articles

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 Journal Methods Collections JoVE Encyclopedia of Experiments 存档

教育

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual 教师资源中心教师网站

使用条款与条件

相关概念视频

RNA-seq

RNA-seq

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...

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

How to benchmark medical AI agents.

PLoS medicine·2026

Same author

AI-based selection of tumor regions for genomic profiling in neuropathology.

Neuro-oncology advances·2026

Same author

Clinical decision support in hematological malignancies using a case-grounded AI agent.

Nature medicine·2026

Same author

A deep learning framework for efficient pathology image analysis.

Nature communications·2026

Same author

Immunomodulatory endothelial cells contribute to T cell recruitment and activation via antigen presentation on MHC II.

Cardiovascular research·2026

Same author

Functional Outcome Prediction in Young Adults With Mental Health Symptoms Using Machine Learning and Large Language Models: Longitudinal Observational Study.

JMIR mental health·2026

Search research articles

相关实验视频

Updated: May 4, 2026

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Published on: May 22, 2017

SwarmMAP:在单细胞测序数据中进行分散的细胞类型注释的群体学习.

Oliver Lester Saldanha¹, Vivien Goepp², Kevin Pfeiffer¹

¹Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Fetscherstraße 74, Dresden, 01307, Saxony, Germany.

bioRxiv : the preprint server for biology

|January 27, 2025

概括

使用Swarm Learning,SwarmMAP自动化了单细胞测序数据的细胞类型注释. 这种去中心化的方法可以提高准确性和隐私性,而无需共享原始数据.

关键词:

细胞类型注释细胞类型注释分类分类分类分类.分散式的学习学习单细胞RNA转录组学单细胞RNA转录组学群体学习群体学习

更多相关视频

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Author Spotlight: Integrating Organoid Models with Single-Cell and Spatial Transcriptomics Technologies

Author Spotlight: Integrating Organoid Models with Single-Cell and Spatial Transcriptomics Technologies

Published on: March 29, 2024

相关实验视频

Last Updated: May 4, 2026

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Published on: May 22, 2017

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Author Spotlight: Integrating Organoid Models with Single-Cell and Spatial Transcriptomics Technologies

Author Spotlight: Integrating Organoid Models with Single-Cell and Spatial Transcriptomics Technologies

Published on: March 29, 2024

科学领域:

计算生物学计算生物学
基因组学就是基因组学.
机器学习机器学习

背景情况:

单细胞转录基因数据生成正在迅速发展,使大规模组织分析成为可能.
目前的细胞类型注释依赖于标记基因的手动检查,这是不一致的,难以扩展的.
患者隐私是人类单细胞数据集的一个重要问题.

研究的目的:

开发一种标准化和自动化的方法,用于单细胞测序数据的细胞类型注释.
通过实现不需要交换原始数据的去中心化分析来解决隐私问题.
评估基于Swarm学习的新型细胞类型分类方法的性能.

主要方法:

开发了SwarmMAP,这是一个利用Swarm Learning进行分散式机器学习模型培训的工具.
训练模型单细胞测序数据从人类的心脏,肺和乳腺组织.
分散式Swarm学习模型与集中式培训的性能比较.

主要成果:

SwarmMAP获得了高的F1分数:0.93 (心脏),0.98 (肺) 和0.88 (乳房).
群体学习模型的平均性能为0.907,与集中式模型相比 (p值=0.937).
数据集的数量增加提高了预测准确性和细胞类型多样性处理.

结论:

在单细胞分析中,Swarm Learning提供了一种可行的,保护隐私的方法,用于自动化单细胞类型注释.
斯沃姆马普展示了去中心化学习的潜力,用于可扩展和可重复的单细胞数据分析.
该方法方便在各种数据集中进行稳健的细胞类型分类.