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

Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

127
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
127
Protein Networks02:26

Protein Networks

4.0K
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,...
4.0K

You might also read

Related Articles

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

Sort by
Same author

Differentially Expressed Gene Annotator (DEGAn): automated annotation and analysis of DEGs datasets with OS and PFS data.

Bioinformatics advances·2026
Same author

CoRTE: a web-service for constructing temporal networks from genotype-tissue expression data.

Bioinformatics advances·2025
Same author

Artificial Intelligence in Cardiac Electrophysiology: A Clinically Oriented Review with Engineering Primers.

Bioengineering (Basel, Switzerland)·2025
Same author

Ten practical tips and tricks to improve the effectiveness of biological network alignment.

PLoS computational biology·2025
Same author

Genetic Biomarkers of Sorafenib Response in Patients with Hepatocellular Carcinoma.

International journal of molecular sciences·2024
Same author

An Exploratory Application of Multilayer Networks and Pathway Analysis in Pharmacogenomics.

Genes·2023

Related Experiment Video

Updated: Jul 30, 2025

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

6.0K

A Method Based on Temporal Embedding for the Pairwise Alignment of Dynamic Networks.

Pietro Cinaglia1, Mario Cannataro2

  • 1Department of Health Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy.

Entropy (Basel, Switzerland)
|May 16, 2023
PubMed
Summary
This summary is machine-generated.

Dynamic Network Alignment based on Temporal Embedding (DANTE) improves knowledge transfer between species by aligning dynamic biological networks. This novel method enhances accuracy and robustness in network analysis, outperforming existing approaches.

Keywords:
dynamic networksembeddingsnetwork alignmenttemporal embeddingtemporal networks

More Related Videos

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
08:36

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

7.3K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.2K

Related Experiment Videos

Last Updated: Jul 30, 2025

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

6.0K
Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
08:36

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

7.3K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.2K

Area of Science:

  • Computational Biology and Bioinformatics
  • Network Science and Graph Theory
  • Systems Biology

Background:

  • Biological systems are often modeled as dynamic networks, representing entities like genes and proteins and their interactions over time.
  • Network alignment is crucial for transferring biological knowledge across species, typically from simpler to more complex organisms.
  • Existing methods for dynamic network alignment face challenges in accurately capturing topological similarities and evolutionary changes.

Purpose of the Study:

  • To introduce Dynamic Network Alignment based on Temporal Embedding (DANTE), a novel method for pairwise dynamic network alignment.
  • To investigate topological similarities between dynamic networks by considering their temporal evolution and interaction changes.
  • To enhance the accuracy, precision, and robustness of knowledge transfer through improved network alignment.

Main Methods:

  • DANTE employs temporal embedding to represent dynamic networks and capture their evolutionary dynamics.
  • A similarity matrix is constructed by integrating tensors derived from the embedding process.
  • Node pairs are aligned using an iterative maximization function applied to the similarity matrix.

Main Results:

  • DANTE demonstrated significant improvements in precision and accuracy, with robust performance as network size and time points increased.
  • The method achieved an optimal trade-off between sensitivity and specificity, improving the Area Under the ROC Curve (AUROC) by approximately 18.8% compared to DYNAMAGNA++ and DYNAWAVE on noisy yeast networks.
  • DANTE showed substantial gains in alignment quality (∼91% for node increase, ∼75% for time point increase) and node correctness (∼23.73%) on real dynamic networks.

Conclusions:

  • DANTE represents a significant advancement in dynamic network alignment, effectively capturing temporal and topological features.
  • The method provides superior performance in knowledge transfer across species compared to existing state-of-the-art techniques.
  • DANTE offers a robust and accurate solution for analyzing complex biological networks and generating novel biological hypotheses.