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

Convolution Properties II01:17

Convolution Properties II

583
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
583
¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)01:20

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)

1.7K
When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...
1.7K
Insensitive Nuclei Enhanced by Polarization Transfer (INEPT)01:15

Insensitive Nuclei Enhanced by Polarization Transfer (INEPT)

1.0K
Insensitive Nuclei Enhanced by Polarization Transfer (INEPT) is an advanced Nuclear Magnetic Resonance (NMR) technique specifically designed to detect and enhance the signals of low-abundance nuclei, such as carbon-13 and nitrogen-15, in small molecules. The fundamental principle behind INEPT is the transfer of polarization from a more abundant and highly polarizable nucleus, typically hydrogen-1, to the low-abundance nucleus of interest. This process effectively boosts the NMR signal of the...
1.0K
Protein Networks02:26

Protein Networks

4.5K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.5K
Convolution Properties I01:20

Convolution Properties I

584
Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
584
Ogive Graph01:07

Ogive Graph

6.8K
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...
6.8K

También podría leer

Artículos Relacionados

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

Ordenar por
Same author

Ion-Driving Polymer Entanglement for Dynamic Organic Phosphorescence.

Angewandte Chemie (International ed. in English)·2026
Same author

Commentary on "Dynamic Right Ventricular Reserve May Refine Post-LVAD Risk Prediction".

Angiology·2026
Same author

Artemisinin oligomers from a natural product to multivalent antimalarial and anticancer agents: Overcoming drug resistance and expanding therapeutic potential.

European journal of medicinal chemistry·2026
Same author

Qishen Yiqi dripping pills alleviate myocardial ischemia-reperfusion-induced fibrotic injury by inhibiting fibroblast activation via the transforming growth factor-beta/Periostin pathway.

Journal of ethnopharmacology·2026
Same author

Dynamical Control of Quantum Photon-Photon Interaction with Phase Change Material.

Physical review letters·2026
Same author

A yolk sac-derived hepatic γδ T cell subpopulation develops in a hematopoietic stem cell- and thymus-independent manner.

Protein & cell·2026
Same journal

conMItion: an R package adjusting confounding factors for associations in multi-omics.

Bioinformatics (Oxford, England)·2026
Same journal

SpaMFG: a Spatial Multi-omics Integration Method based on Feature Grouping.

Bioinformatics (Oxford, England)·2026
Same journal

CSCN: Inference of Cell-Specific Causal Networks Using Single-Cell RNA-Seq Data.

Bioinformatics (Oxford, England)·2026
Same journal

Sparse CCA-Based Mediation Analysis with High-Dimensional Exposures and Mediators.

Bioinformatics (Oxford, England)·2026
Same journal

Enhancing Cross-Context Generalization in Drug Perturbation Prediction with a Multimodal Conditional Diffusion Framework.

Bioinformatics (Oxford, England)·2026
Same journal

Primer Design through Submodular Function Estimation.

Bioinformatics (Oxford, England)·2026
Ver todos los artículos relacionados

Video Experimental Relacionado

Updated: Jan 29, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

706

STransfer: Una red convolucional de grafos mejorada por aprendizaje por transferencia para la agrupación de datos de

Chaojie Wang1, Xin Yu2

  • 1School of Mathematical Sciences, Jiangsu University, Zhenjiang, Jiangsu 212013, China.

Bioinformatics (Oxford, England)
|January 28, 2026
PubMed
Resumen
Este resumen es generado por máquina.

STransfer, un nuevo marco de aprendizaje por transferencia, mejora el análisis de transcriptómica espacial integrando redes convolucionales de grafos y la información mutua positiva punto por punto. Este método mejora la precisión de la agrupación y el modelado espacial en cortes de tejido.

Palabras clave:
transcriptómica espacialredes convolucionales de grafosmúltiples cortesaprendizaje por transferencia

Más Videos Relacionados

Spatial Separation of Molecular Conformers and Clusters
10:37

Spatial Separation of Molecular Conformers and Clusters

Published on: January 9, 2014

11.8K
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.5K

Videos de Experimentos Relacionados

Last Updated: Jan 29, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

706
Spatial Separation of Molecular Conformers and Clusters
10:37

Spatial Separation of Molecular Conformers and Clusters

Published on: January 9, 2014

11.8K
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.5K

Área de la Ciencia:

  • Biología computacional
  • Genómica
  • Bioinformática

Sus antecedentes:

  • El análisis de la transcriptómica espacial es crucial para comprender la arquitectura del tejido.
  • Los métodos existentes a menudo pasan por alto las similitudes entre cortes en conjuntos de datos de múltiples cortes.
  • La captura precisa de la estructura espacial es fundamental para la obtención de información biológica.

Objetivo del estudio:

  • Desarrollar un marco novedoso de aprendizaje por transferencia para la transcriptómica espacial.
  • Abordar las limitaciones de los métodos actuales modelando las similitudes entre cortes.
  • Mejorar la precisión de la agrupación y reducir la anotación manual en la transcriptómica espacial.

Principales métodos:

  • Se propuso el marco STransfer que combina redes convolucionales de grafos (GCN) e información mutua positiva punto por punto (PPMI).
  • Se utilizó un módulo basado en atención para fusionar características multigrafo en representaciones de nodos unificadas.
  • Se desarrollaron incrustaciones de baja dimensión que codifican la expresión génica y el contexto espacial.

Principales resultados:

  • STransfer modela eficazmente las dependencias espaciales locales y globales.
  • El marco transfiere con éxito el conocimiento de cortes de tejido etiquetados a no etiquetados.
  • Se logró una precisión de agrupación y un modelado espacial superiores en comparación con los métodos de vanguardia.

Conclusiones:

  • STransfer ofrece una solución robusta para el análisis de datos de transcriptómica espacial.
  • El método mejora la comprensión de los patrones de expresión génica espacial.
  • STransfer mejora la eficiencia al reducir los esfuerzos de anotación manual.