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

12.9K
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...
12.9K
Confocal Fluorescence Microscopy01:16

Confocal Fluorescence Microscopy

14.6K
Confocal microscopy is an advanced microscopic technique. The prime advantage of the confocal microscope over other microscopy techniques is its ability to block the out-of-focus light from the illuminated samples using pinholes. It is widely used with fluorescence optics to obtain high-resolution, sharp contrast images. Unlike optical microscopes, confocal microscopes use a focused beam of light laser to scan the entire sample surface at different z-planes. These microscopes are, therefore,...
14.6K

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

Synthetic plasma pool cohort correction for affinity-based proteomics datasets allows multiple study comparison.

Briefings in bioinformatics·2024
Same author

Fibroblast-like synoviocyte targeting antibodies are associated with failure to reach early and sustained remission or low disease activity after first-line therapy in rheumatoid arthritis.

RMD open·2024
Same author

Snowflake: visualizing microbiome abundance tables as multivariate bipartite graphs.

Frontiers in bioinformatics·2024
Same author

BioMOBS: A multi-omics visual analytics workflow for biomolecular insight generation.

PloS one·2023
Same author

Novel maternal autoantibodies in autism spectrum disorder: Implications for screening and diagnosis.

Frontiers in neuroscience·2023
Same author

Exploring the Microbiome Analysis and Visualization Landscape.

Frontiers in bioinformatics·2022
Same journal

DARUMA: a gateway to fast and easy prediction of intrinsically disordered regions.

PeerJ. Computer science·2026
Same journal

Alzheimer's disease detection using a quantum deep neural network with Haralick feature extraction and simulated annealing optimization.

PeerJ. Computer science·2026
Same journal

Network anomaly detection using Deep Autoencoder and parallel Artificial Bee Colony algorithm-trained neural network.

PeerJ. Computer science·2026
Same journal

An anomaly detection model for multivariate time series with anomaly perception.

PeerJ. Computer science·2026
Same journal

Retraction: A wormhole attack detection method for tactical wireless sensor networks.

PeerJ. Computer science·2026
Same journal

Evaluation of mental disorder with prioritization of its type by utilizing the bipolar complex fuzzy decision-making approach based on Schweizer-Sklar prioritized aggregation operators.

PeerJ. Computer science·2026
查看所有相关文章

相关实验视频

Updated: Sep 18, 2025

Rapid Analysis and Exploration of Fluorescence Microscopy Images
11:41

Rapid Analysis and Exploration of Fluorescence Microscopy Images

Published on: March 19, 2014

12.5K

FLASC:一种对闪光灯敏感的集群算法.

Daniël M Bot1, Jannes Peeters1, Jori Liesenborgs2

  • 1Data Science Institute (DSI), Universiteit Hasselt, Diepenbeek, Belgium.

PeerJ. Computer science
|June 26, 2025
PubMed
概括
此摘要是机器生成的。

闪光敏感集群 (FLASC) 通过检测分支来识别数据集群中的基于形状的子组. 这种新的算法通过揭示复杂的数据结构来增强探索性数据分析,基于现有的基于密度的方法.

关键词:
分支层次检测检测分支层次检测基于密度的聚类.探索性数据分析数据分析.在HDBSCAN*中使用.

更多相关视频

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
05:12

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

11.5K
Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore
06:01

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Published on: December 12, 2019

8.6K

相关实验视频

Last Updated: Sep 18, 2025

Rapid Analysis and Exploration of Fluorescence Microscopy Images
11:41

Rapid Analysis and Exploration of Fluorescence Microscopy Images

Published on: March 19, 2014

12.5K
ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
05:12

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

11.5K
Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore
06:01

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Published on: December 12, 2019

8.6K

科学领域:

  • 数据科学数据科学数据科学
  • 机器学习 机器学习
  • 计算统计学 计算统计学

背景情况:

  • 聚类算法对于探索性数据分析至关重要,它可以将类似的数据点分组在一起.
  • 集群形状,如Y形形成,可以表示不断演变的过程和不同的结果.
  • 现有的基于密度的集群方法,如HDBSCAN*,不能明确识别分支结构.

研究的目的:

  • 引入易燃集群 (FLASC),一种用于检测数据集群中的分支的新算法.
  • 能够识别基于形状的子组,这些子组代表有意义的数据模式.
  • 增强基于密度的聚类功能,用于复杂的数据分析.

主要方法:

  • FLASC基于HDBSCAN*算法构建,包含一个后处理步骤来检测分支.
  • 通过分析集群内部连接来实现分支检测.
  • 介绍了FLASC的两个变体,在计算成本和噪声稳定性之间提供了不同的权衡.

主要成果:

  • FLASC变体显示了与HDBSCAN*可比的计算扩展.
  • 该算法在多个运行中产生一致的输出.
  • 使用FLASC的分支检测在两个真实数据集上证明是有益的,揭示了以前未识别的子组.

结论:

  • FLASC有效地识别了集群中的分支结构,为数据提供了更深入的见解.
  • 该算法通过发现基于形状的子组来增强探索性数据分析.
  • FLASC为基于密度的聚类提供了一个有价值的扩展,在Python中提供了实现.