Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Improving Translational Accuracy02:07

Improving Translational Accuracy

2.6K
2.6K
Maxam-Gilbert Sequencing01:05

Maxam-Gilbert Sequencing

11.2K
In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
Challenges of the Maxam-Gilbert Method
The...
11.2K
Structure of a Gene01:30

Structure of a Gene

12.6K
A gene is the fundamental unit of heredity. Every individual has two copies of each gene, one inherited from each parent. Although most people contain the same genes, there is a small fraction that is slightly different amongst people. A gene with a small difference in its sequence of DNA bases forms different alleles, contributing to different phenotypes.
However, only 1% of the DNA is composed of genes that encode proteins; the rest, 99% is non-coding DNA. This non-coding DNA performs...
12.6K
Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

12.2K
Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
12.2K
Nucleic Acid Structure01:25

Nucleic Acid Structure

6.2K
The pentose sugar in DNA is deoxyribose, while in RNA the pentose sugar is ribose. The difference between the sugars is the presence of the hydroxyl group on the ribose's second carbon and a hydrogen on the deoxyribose's second carbon. The phosphate residue attaches to the hydroxyl group of the 5′ carbon of one sugar and the hydroxyl group of the 3′ carbon of the sugar of the next nucleotide, which forms  a 5′ to 3′ phosphodiester linkage.
DNA Structure
DNA...
6.2K
Chromosome Structure02:40

Chromosome Structure

4.8K
4.8K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Exogenous melatonin alleviates post-flowering natural high-temperature stress in maize (<i>Zea mays</i> L.) by promoting photosynthesis and antioxidant defense responses.

Frontiers in plant science·2026
Same author

Liquid-Phase Synthesis and Regulatory Mechanisms of Nano-Nickel Powders for MLCC Inner Electrodes.

Nanomaterials (Basel, Switzerland)·2026
Same author

Analysis of influencing factors on the plastic zone width and stability of aeolian sand-based backfill strips.

Scientific reports·2026
Same author

Optimizing water and nitrogen management in a wheat-maize rotation system: synergistic increases in grain yield, resource use efficiency, and economic and environmental benefits.

Frontiers in plant science·2026
Same author

The mediating role of activities of daily living in the association between intrinsic capacity and health-related quality of life: evidence from the WHO ICOPE pilot in China.

Aging clinical and experimental research·2026
Same author

Hyperspectral remote sensing image classification based on domain-level complementarity of spatial-spectral component.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Jul 20, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

291

Improved Skip-Gram Based on Graph Structure Information.

Xiaojie Wang1, Haijun Zhao1, Huayue Chen1

  • 1School of Computer Science, China West Normal University, Nanchong 637002, China.

Sensors (Basel, Switzerland)
|July 29, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces Graph Skip-gram, a novel graph representation learning method extending the Skip-gram model. It enhances graph embedding by incorporating graph structure information for improved node vector analysis and feature fusion.

Keywords:
Skip-gramgraph embeddinggraph structureinterpretabilitynode feature fusion

More Related Videos

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

7.3K
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

623

Related Experiment Videos

Last Updated: Jul 20, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

291
Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

7.3K
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

623

Area of Science:

  • Computer Science
  • Machine Learning
  • Graph Theory

Background:

  • Skip-gram models are widely used in natural language processing for word embeddings.
  • Graph representation learning aims to learn low-dimensional representations of nodes in a graph.
  • Existing research often migrates Skip-gram to graph learning without fully exploring its graph-specific applications.

Purpose of the Study:

  • To address the insufficient exploration of Skip-gram in graph representation learning.
  • To analyze the differences between word embedding and graph embedding.
  • To propose an effective graph representation learning method inspired by Skip-gram.

Main Methods:

  • Analyzed the principles of graph representation learning through a case study.
  • Developed Graph Skip-gram, an extension of the Skip-gram model incorporating graph structure.
  • Designed a novel feature fusion algorithm based on node vector similarity.

Main Results:

  • Demonstrated the intuitive idea of graph embedding through a case study.
  • Graph Skip-gram showed excellent adaptability with various algorithms.
  • Experimental comparisons on classification and link prediction tasks validated the approach's applicability.

Conclusions:

  • Graph Skip-gram is a more applicable extension of Skip-gram for graph representation learning.
  • The proposed feature fusion algorithm effectively utilizes node vector similarity.
  • This work provides a deeper understanding and practical application of Skip-gram in graph domains.