Jove
Visualize
Contáctanos
JoVE
x logofacebook logolinkedin logoyoutube logo
ACERCA DE JoVE
Visión GeneralLiderazgoBlogCentro de Ayuda JoVE
AUTORES
Proceso de PublicaciónConsejo EditorialAlcance y PolíticasRevisión por ParesPreguntas FrecuentesEnviar
BIBLIOTECARIOS
TestimoniosSuscripcionesAccesoRecursosConsejo Asesor de BibliotecasPreguntas Frecuentes
INVESTIGACIÓN
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchivo
EDUCACIÓN
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualCentro de Recursos para ProfesoresSitio de Profesores
Términos y Condiciones de Uso
Política de Privacidad
Políticas

Videos de Conceptos Relacionados

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

También podría leer

Artículos Relacionados

Artículos vinculados a este trabajo por autores compartidos, revista y gráfico de citas.

Ordenar por
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

Another 10 years of PLOS Computational Biology: A data-driven reflection on trends in genomics research.

PLoS computational biology·2026
Same journal

Mobility data resolution needed to inform predictive models of spatial epidemic spread from mobile phone data.

PLoS computational biology·2026
Same journal

DeepMethylation: A deep learning framework for tissue-specific DNA methylation prediction and functional variant annotation.

PLoS computational biology·2026
Same journal

Redefining and estimating the early-phase reproduction ratio for epidemic outbreaks in spatially structured populations.

PLoS computational biology·2026
Same journal

Optimized phenotype definitions boost GWAS power.

PLoS computational biology·2026
Same journal

Detection, communication, and individual identification with deep audio embeddings: A case study with North Atlantic right whales.

PLoS computational biology·2026
Ver todos los artículos relacionados

Video Experimental Relacionado

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: Aprendizaje multimodal de grafos para el análisis de transcriptómica espacial con datos genéticos y

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
Resumen
Este resumen es generado por máquina.

El aprendizaje aumentado por convolución de grafos (AugGCL) mejora la transcriptómica espacial integrando datos de expresión génica e imágenes. Este novedoso marco mejora la reconstrucción del dominio espacial, superando desafíos como la escasez y las señales débiles para un mejor análisis tisular.

Palabras clave:
transcriptómica espacialaprendizaje profundoaprendizaje automáticogenómica computacionalanálisis de datos multimodales

Más Videos Relacionados

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

Videos de Experimentos Relacionados

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

Sus antecedentes:

  • La transcriptómica espacial ofrece información sobre la expresión génica en tejidos intactos.
  • La reconstrucción de dominios espaciales precisos es un desafío debido a la escasez de expresión, la compleja arquitectura tisular y las señales débiles.
  • Los métodos existentes que utilizan agrupamiento y suavizado tienen un rendimiento inferior en los límites y en regiones escasas, sin tener en cuenta la morfología.

Conclusiones:

  • AugGCL genera dominios espaciales más claros, avanzando en las aplicaciones de transcriptómica espacial.
  • El marco mejora eficazmente las señales espaciales débiles y agudiza los límites.
  • AugGCL contribuye significativamente a la investigación de la estructura tisular y las enfermedades utilizando la transcriptómica espacial.