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

Cluster Sampling Method01:20

Cluster Sampling Method

11.8K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
11.8K
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

1.5K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
1.5K
Vesicular Tubular Clusters01:45

Vesicular Tubular Clusters

2.5K
After budding out from the ER membrane, some COPII vesicles lose their coat and fuse with one another to form larger vesicles and interconnected tubules called vesicular tubular clusters or VTCs. These clusters constitute a compartment at the ER-Golgi interface known as ERGIC (Endoplasmic Reticulum Golgi Intermediate Compartment). The ERGIC is a mobile membrane-bound cargo transport system that sorts proteins secreted from ER and delivers them to the Golgi.
With the help of motor proteins such...
2.5K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

162
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
162
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
Aggregates Classification01:29

Aggregates Classification

309
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
309

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

Ultra-low concentration PVA-doped PMMA: an all-organic dielectric with markedly improved dielectric properties and energy storage performance.

RSC advances·2026
Same author

High Breakdown and Energy Storage Properties of PMMA via Conformational Transition Induced by a Heat Treatment Strategy.

The journal of physical chemistry. B·2026
Same author

Vitamin A Emulsion Encapsulated with Whey Protein Isolate-Soybean Lecithin Enhances Surimi Gel Structure and Protein Conformation.

Foods (Basel, Switzerland)·2025
Same author

Multimodal Sensing Smart Skin System for Wide-Range and High-Sensitivity Stress Detection.

ACS sensors·2025
Same author

China's future food demand forecast based on provincial diets and shared socio-economic pathways.

Scientific data·2025
Same author

Dupilumab-induced inhibition of myeloid dendritic cell function via TIM-3-TGF-β1 feedback loop in treatment of atopic dermatitis.

The Journal of allergy and clinical immunology·2025

相关实验视频

Updated: Jun 14, 2025

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

6.9K

强大的子集群搜索和合并集群.

Bocheng Wang, Mulin Chen, Xuelong Li

    IEEE transactions on cybernetics
    |September 4, 2024
    PubMed
    概括

    强大的子集群搜索和合并 (RSSM) 通过利用异常值来识别子中心点来改进基于图的集群. 这种方法增强了数据结构的学习,并产生了一个更适合准确集群的图形.

    科学领域:

    • 数据科学数据科学数据科学
    • 机器学习 机器学习
    • 人工智能的人工智能

    背景情况:

    • 基于图形的聚类通过分割相似度图来分割数据.
    • 现有的方法与现实数据固有的噪音和异常值作斗争.
    • 当前的方法往往直接从学习的图表中导出集群,需要严格的内部数据分布.

    研究的目的:

    • 引入一个新的集群模型,强大的子集群搜索和合并 (RSSM).
    • 解决现有的基于图形的集群方法的局限性,特别是关于异常值处理.
    • 提高学习图的质量,以实现更有效的集群.

    主要方法:

    • RSSM利用异常值,灵感来自积极激励噪声 (Pi-Noise),用于结构学习.
    • 它通过搜索不平衡的残留分布来确定子中心点,将内置值与异常值分开.
    • 构建一个子集群相似度图以指导已识别的子集群的合并.

    主要成果:

    • 由子中心体识别的子集群在正常样本中表现出更紧密的联系.
    • RSSM有效地利用异常值来完善用于集群的图形结构.
    • 实验结果验证了RSSM模型的合理性和优越性.

    更多相关视频

    A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'
    10:31

    A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'

    Published on: February 10, 2017

    11.1K
    Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
    07:12

    Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

    Published on: July 1, 2014

    12.3K

    相关实验视频

    Last Updated: Jun 14, 2025

    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

    6.9K
    A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'
    10:31

    A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'

    Published on: February 10, 2017

    11.1K
    Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
    07:12

    Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

    Published on: July 1, 2014

    12.3K

    结论:

    • 通过有效处理异常值,RSSM提供了基于图形的集群的强有力的方法.
    • 同时搜索和合并子集群,在异常值的帮助下,可以提高集群性能.
    • 拟议的方法产生了一个更适合的图形表示集群任务.