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

508
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...
508
Dynamic Equilibrium02:20

Dynamic Equilibrium

64.0K
A reversible chemical reaction represents a chemical process that proceeds in both forward (left to right) and reverse (right to left) directions. When the rates of the forward and reverse reactions are equal, the concentrations of the reactant and product species remain constant over time and the system is at equilibrium. A special double arrow is used to emphasize the reversible nature of the reaction. The relative concentrations of reactants and products in equilibrium systems vary greatly;...
64.0K
Protein Networks02:26

Protein Networks

4.6K
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,...
4.6K
Protein Networks02:26

Protein Networks

2.9K
2.9K
Cytoskeletal Coordination in Cell Migration01:32

Cytoskeletal Coordination in Cell Migration

5.6K
A migrating cell changes its shape during the cyclic events of attachment and detachment from the substratum and repositions the cell organelles correspondingly. These complex events are orchestrated by the dynamic cytoskeletal network comprising actin filaments, intermediate filaments, and microtubules. Cytoskeletal crosstalk — the direct and indirect communication between the different components — is crucial for this coordination. Direct communication involves various linker...
5.6K
Design Example: Alignment of a Road Line Using GIS01:17

Design Example: Alignment of a Road Line Using GIS

365
The alignment of a road line using Geographic Information Systems (GIS) is a critical process in civil engineering, combining advanced technology with practical decision-making. This methodology begins with the collection of geospatial data, including information on land cover, geomorphology, drainage patterns, slope, and contour details. Such data is typically acquired through satellite imagery and GIS tools, offering a comprehensive understanding of the terrain.Once the data is gathered, it...
365

You might also read

Related Articles

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

Sort by
Same author

AZ63/Ti/Zr Nanocomposite for Bone-Related Biomedical Applications.

BioMed research international·2023
Same author

Modelling and optimization of thermophilic anaerobic digestion using biowaste.

Environmental research·2022
Same author

An Investigation of Electrospun <i>Clerodendrum phlomidis</i> Leaves Extract Infused Polycaprolactone Nanofiber for In Vitro Biological Application.

Bioinorganic chemistry and applications·2022
Same author

Synthesis and Characterization of Banana and Pineapple Reinforced Hybrid Polymer Composite for Reducing Environmental Pollution.

Bioinorganic chemistry and applications·2022
Same author

Ambiguous Genitalia and Lissencephaly in A 46,XY Neonate with a Novel Variant of Aristaless Gene.

Acta endocrinologica (Bucharest, Romania : 2005)·2022
Same author

Investigations on influences of MWCNT composite membranes in oil refineries waste water treatment with Taguchi route.

Chemosphere·2022
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
See all related articles

Related Experiment Video

Updated: Feb 23, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.7K

Alignment of dynamic networks.

V Vijayan1, D Critchlow1,2, T Milenkovic1

  • 1Department of Computer Science and Engineering, ECK Institute for Global Health, and Interdisciplinary Center for Network Science and Applications (iCeNSA), University of Notre Dame, Notre Dame, IN, USA.

Bioinformatics (Oxford, England)
|September 9, 2017
PubMed
Summary
This summary is machine-generated.

Dynamic network alignment (NA) methods, like DynaMAGNA++, outperform static NA by conserving evolving network structures. This approach enhances knowledge transfer in fields such as computational biology.

More Related Videos

Analyzing the Size, Shape, and Directionality of Networks of Coupled Astrocytes
10:10

Analyzing the Size, Shape, and Directionality of Networks of Coupled Astrocytes

Published on: October 4, 2018

9.4K
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

1.6K

Related Experiment Videos

Last Updated: Feb 23, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.7K
Analyzing the Size, Shape, and Directionality of Networks of Coupled Astrocytes
10:10

Analyzing the Size, Shape, and Directionality of Networks of Coupled Astrocytes

Published on: October 4, 2018

9.4K
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

1.6K

Area of Science:

  • Computational biology
  • Network science
  • Systems biology

Background:

  • Network alignment (NA) is crucial for transferring biological knowledge between species.
  • Existing NA methods only analyze static networks, failing to capture system dynamics.
  • Real-world systems are dynamic and require methods that account for temporal evolution.

Purpose of the Study:

  • To introduce the first dynamic NA method, DynaMAGNA++.
  • To test the hypothesis that dynamic NA yields superior alignments over static NA.
  • To develop novel measures for dynamic edge and node conservation.

Main Methods:

  • DynaMAGNA++ extends the state-of-the-art static NA method MAGNA++.
  • It optimizes conservation of dynamic edges (events) and evolving node neighborhoods.
  • Introduces a new measure for dynamic edge conservation and utilizes a dynamic node conservation measure.

Main Results:

  • Dynamic NA, as implemented in DynaMAGNA++, is superior to static NA.
  • Confirmed hypothesis on synthetic and real-world networks across computational biology and social domains.
  • DynaMAGNA++ is parallelized and includes a graphical interface.

Conclusions:

  • Dynamic network alignment is essential for accurately modeling and analyzing evolving systems.
  • DynaMAGNA++ provides a robust framework for dynamic NA, improving upon static methods.
  • The developed dynamic conservation measures are adaptable to other state-of-the-art NA methods.