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

Ogive Graph01:07

Ogive Graph

6.7K
An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
6.7K
Graphing Antiderivatives01:30

Graphing Antiderivatives

51
The concept of an antiderivative is fundamental in calculus, describing how a function's values accumulate over time. This process is closely related to physical motion, such as the movement of a rolling ball. As the ball progresses, its position changes in response to variations in velocity, just as an antiderivative graph reflects the cumulative effect of the original function's values.Graphing an antiderivative requires interpreting how a function's values influence the shape of its...
51
Bar Graph01:07

Bar Graph

21.5K
A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
21.5K
Time-Series Graph00:54

Time-Series Graph

5.0K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
5.0K
Multiple Bar Graph01:07

Multiple Bar Graph

9.0K
As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
9.0K
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

694
Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
694

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

Multi-layer matrix factorization for cancer subtyping using full and partial multi-omics dataset.

Briefings in bioinformatics·2025
Same author

Multi-view multi-level contrastive graph convolutional network for cancer subtyping on multi-omics data.

Briefings in bioinformatics·2025
Same author

Research on Predicting the Turnover of Graduates Using an Enhanced Random Forest Model.

Behavioral sciences (Basel, Switzerland)·2024
Same author

MRGCN: cancer subtyping with multi-reconstruction graph convolutional network using full and partial multi-omics dataset.

Bioinformatics (Oxford, England)·2023
Same author

Deep structure integrative representation of multi-omics data for cancer subtyping.

Bioinformatics (Oxford, England)·2022
Same author

Risk factors for chemotherapy-induced vomiting after general anesthesia in children with retinoblastoma: a retrospective study.

Translational pediatrics·2022
Same journal

Systematic design of auxotrophic strains and media conditions to probe metabolic functions in E. coli.

PLoS computational biology·2026
Same journal

Neuronal excitability and parameter variability in the Hodgkin-Huxley model.

PLoS computational biology·2026
Same journal

Delayed reward information is underweighted in reinforcement learning with dispersed feedback.

PLoS computational biology·2026
Same journal

GHF-ACL: A novel contrastive learning framework with multi-order graph structures for herb-disease association prediction.

PLoS computational biology·2026
Same journal

GATE: Adaptive learning with working memory by information gating in multi-lamellar hippocampal formation.

PLoS computational biology·2026
Same journal

Evaluating vectors for the design of a spillover-disrupting Lassa virus transmissible vaccine.

PLoS computational biology·2026
查看所有相关文章

相关实验视频

Updated: Jan 25, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

687

AugGCL:多模式图形学习用于空间转录组学分析,增强了基因和形态数据.

Tengfei Ji1, Bo Yang1, Meng Wang1

  • 1School of Computer Science, Xi'an Polytechnic University, Xi'an, Shaanxi, China.

PLoS computational biology
|January 23, 2026
PubMed
概括
此摘要是机器生成的。

增强图形卷积学习 (AugGCL) 通过整合基因表达和图像数据来改善空间转录学. 这种新的框架增强了空间域的重建,克服了诸如稀疏和弱信号等挑战,以便更好地分析组织.

更多相关视频

Spatially Compact Arrangement of Larval Zebrafish Sections for Spatial Transcriptomic Analysis
07:40

Spatially Compact Arrangement of Larval Zebrafish Sections for Spatial Transcriptomic Analysis

Published on: May 16, 2025

927
Author Spotlight: Exploring Advanced Therapeutic Targets in Osteosarcoma Through Spatial Transcriptomics
07:43

Author Spotlight: Exploring Advanced Therapeutic Targets in Osteosarcoma Through Spatial Transcriptomics

Published on: May 3, 2024

4.4K

相关实验视频

Last Updated: Jan 25, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

687
Spatially Compact Arrangement of Larval Zebrafish Sections for Spatial Transcriptomic Analysis
07:40

Spatially Compact Arrangement of Larval Zebrafish Sections for Spatial Transcriptomic Analysis

Published on: May 16, 2025

927
Author Spotlight: Exploring Advanced Therapeutic Targets in Osteosarcoma Through Spatial Transcriptomics
07:43

Author Spotlight: Exploring Advanced Therapeutic Targets in Osteosarcoma Through Spatial Transcriptomics

Published on: May 3, 2024

4.4K

科学领域:

  • 基因组学就是基因组学.
  • 计算生物学 计算生物学
  • 生物信息学是一种生物信息学.

背景情况:

  • 空间转录学为完整组织中的基因表达提供了洞察力.
  • 重建精确的空间域是具有挑战性的,因为表达稀疏性,复杂的组织架构和弱信号.
  • 使用聚类和光滑的现有方法在边界和稀疏区域的表现不佳,忽视了形态学.

研究的目的:

  • 介绍AugGCL,一个增强的图形卷积学习框架.
  • 为了增强空间结构解码和基因表达重建在空间转录学.
  • 通过整合基因和图像数据来解决传统管道的局限性.

主要方法:

  • AugGCL采用了邻近信息聚合机制,整合了表达式相似性和空间接近性.
  • 一个加权图和增强表达式矩阵被构建以解决稀疏性而不会失去边界清晰度.
  • 一个双流权重图形卷积网络联合模拟基因特征和图像衍生的形态信息,使用图像感知辅助重建.

主要成果:

  • 在多个指标上,AugGCL的表现优于人类前额叶皮层,乳腺癌和小鼠胚胎数据集的基线方法.
  • 该方法在各种数据集中展示了稳定性和概括性.
  • 下游分析证实了细胞注释,功能丰富和机制研究的可靠性.

结论:

  • AugGCL产生了更清晰的空间域,推进了空间转录学应用.
  • 该框架有效地增强了弱空间信号,并加强了边界.
  • AugGCL 通过空间转录组学对组织结构和疾病研究做出了重大贡献.