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

Overview Of Cell Separation And Isolation01:20

Overview Of Cell Separation And Isolation

5.7K
Cell separation was first achieved in 1964 by S. H. Seal, who separated large tumor cells from the smaller blood cells using filtration. Two years later, Pohl and Hawk performed experiments on how cells respond differently to a nonuniform electric field based on the cell type. Such observations were the inception of cell separation methods, which allow isolating a single cell type from a heterogeneous sample.
5.7K
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

5.7K
Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
5.7K

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

Automated Proofreading of Digitally Reconstructed Neural Morphology Enhances Accuracy, Scalability, and Standardization.

bioRxiv : the preprint server for biology·2026
Same author

Visualization and simulation of full-scale point-neuron circuits via the Neural Circuit Visualizer web platform.

Scientific reports·2026
Same author

Hippocampome.org, a resource for subicular neuron types and beyond.

bioRxiv : the preprint server for biology·2026
Same author

Dendritome mapping reveals the spatial organization of striatal neuron morphology.

Nature neuroscience·2025
Same author

Organization and Community Usage of a Neuron Type Circuitry Knowledge Base of the Hippocampal Formation.

Biomedicines·2025
Same author

Biologically-informed excitatory and inhibitory ratio for robust spiking neural network training.

Scientific reports·2025
Same journal

MetaboNet-Bench: A Multi-modal Benchmark for Glucose Forecasting in Type 1 Diabetes.

ArXiv·2026
Same journal

A Positron Range Correction with Texture Preservation Framework in PET Imaging.

ArXiv·2026
Same journal

Automated optimization of force field parameters against ensemble-averaged measurements with Bayesian Inference of Conformational Populations.

ArXiv·2026
Same journal

Droplet Fusion as a Relaxation Process: Comparison with Shape Recovery of Newtonian and Viscoelastic Droplets.

ArXiv·2026
Same journal

Ridge-filter crosstalk in conformal proton FLASH planning: dependence on beamlet pitch and iterative mitigation.

ArXiv·2026
Same journal

Electrochemical DNA Hairpin Sensors for Differentiating Small Molecule Intercalation from Minor Groove Binding.

ArXiv·2026
查看所有相关文章

相关实验视频

Updated: Jun 30, 2025

Fabrication of a Multiplexed Artificial Cellular MicroEnvironment Array
07:19

Fabrication of a Multiplexed Artificial Cellular MicroEnvironment Array

Published on: September 7, 2018

8.5K

一种用于聚类细胞数据的新方法,以改善分类.

Diek W Wheeler1, Giorgio A Ascoli1

  • 1Center for Neural Informatics, Structures, & Plasticity, Krasnow Institute for Advanced Study; and Bioengineering Department, Volgenau School of Engineering; George Mason University, Fairfax, VA 22030-4444, USA.

ArXiv
|March 18, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新协议,用于使用层次聚类和统计测试来分类大型蜂数据集. 它提供了一种客观的方法来确定神经科学等领域的最佳数据细分度.

更多相关视频

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.0K
Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques
09:48

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Published on: June 30, 2017

7.5K

相关实验视频

Last Updated: Jun 30, 2025

Fabrication of a Multiplexed Artificial Cellular MicroEnvironment Array
07:19

Fabrication of a Multiplexed Artificial Cellular MicroEnvironment Array

Published on: September 7, 2018

8.5K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.0K
Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques
09:48

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Published on: June 30, 2017

7.5K

科学领域:

  • 神经科学和计算生物学
  • 数据科学和机器学习

背景情况:

  • 细胞数据在神经科学等领域的快速增长需要有效的组织和解释方法.
  • 层次聚类是分割大型数据集的常见技术,但缺乏确定分类细分性的客观标准.

研究的目的:

  • 通过将无监督的层次聚类与统计测试相结合,提出一种用于分类蜂数据集的协议.
  • 提供一个客观的方法来确定适当的细分度,当细分数据集群.

主要方法:

  • 该协议采用数据驱动的无监督等级集群.
  • 统计测试是集成的,以系统地确定停止集群分类的最佳点.
  • 该方法基于这样一个原则:集群之间的差异应该超过集群内部的差异.

主要成果:

  • 开发的协议为分类蜂数据集提供了一种通用解决方案.
  • 它适用于任何可以用二维数值矩阵表示的数据集 (例如分子,生理,解剖数据).
  • 使用来自 Janelia MouseLight 项目的神经元形态数据证明了有效性.

结论:

  • 该协议提供了一个客观和系统的方法来对大型蜂数据集进行分类.
  • 这种方法增强了复杂的生物数据的解释,特别是在神经科学中.
  • 在各种数据类型中普遍适用,使其成为数据分析的有价值工具.