Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Protein Networks

Protein Networks

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,...

Protein Networks

Protein Networks

Protein-protein Interfaces

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...

Manipulation and Analysis

Manipulation and Analysis

GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...

Interactions Between Signaling Pathways

Interactions Between Signaling Pathways

Signaling cascades usually lack linearity. Multiple pathways interact and regulate one another, allowing cells to integrate and respond to diverse environmental stimuli.
Convergence and divergence, and cross-talk between signaling pathways
Two distinct signaling pathways can converge on a single functional unit, which may either be a single protein or a complex of proteins. The response is either functionally distinct or synergistic between the two pathways but different from the response...

Selected Data About Geographic Locations

Selected Data About Geographic Locations

Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...

You might also read

Related Articles

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

Sort by

Same author

Secure yet fragile: adversarial vulnerabilities of federated vision-language models in medical AI.

Scientific reports·2026

Same author

Taming polarized fitting: BLINEX-Pcomp with asymmetric risk penalty for robust Pcomp classification.

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

Same author

A simple and reliable instance selection for fast training support vector machine: Valid Border Recognition.

Neural networks : the official journal of the International Neural Network Society·2023

Same author

Preface: Selected revised papers from the LION 15 conference.

Annals of mathematics and artificial intelligence·2023

Same author

Scenic routing navigation using property valuation.

Journal of big data·2023

Same author

Diffusion capacity of single and interconnected networks.

Nature communications·2023

Same journal

Zero-shot reconstruction of mutant spatial transcriptomes.

Patterns (New York, N.Y.)·2026

Same journal

Dendritic nonlinearities mitigate communication costs.

Patterns (New York, N.Y.)·2026

Same journal

Erratum: Agentic AI as a coordination paradigm in digital health and agri-food systems.

Patterns (New York, N.Y.)·2026

Same journal

Spacing effect improves generalization in biological and artificial systems.

Patterns (New York, N.Y.)·2026

Same journal

A multi-modal foundation model for brain disease diagnosis and medical imaging.

Patterns (New York, N.Y.)·2026

Same journal

DuoMod-Net: Logarithmic balancing and geometric refinement for imbalanced semi-supervised medical image segmentation.

Patterns (New York, N.Y.)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Nov 29, 2025

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Interdependent Networks: A Data Science Perspective.

M Hadi Amini^1,2, Ahmed Imteaj^1,2, Panos M Pardalos³

¹School of Computing and Information Sciences, Florida International University, Miami, FL 33199, USA.

Patterns (New York, N.Y.)

|November 18, 2020

Summary

This summary is machine-generated.

Smart cities increasingly use interdependent networks, requiring new data analytics for agent-based decision-making. This study explores challenges and solutions for reliable communication in these complex, multi-layered systems.

Keywords:

data science energy network financial network healthcare network heterogeneity interdependent decision making interdependent networks large-scale optimization problem multiplex networks societal network transportation network water network

More Related Videos

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

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

Published on: December 7, 2021

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Published on: September 25, 2021

Related Experiment Videos

Last Updated: Nov 29, 2025

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

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

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

Published on: December 7, 2021

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Published on: September 25, 2021

Area of Science:

Network Science
Data Science
Smart City Technologies

Background:

Traditionally, networks were analyzed independently.
Smart city advancements have increased network interdependence.
Interdependent networks feature shared decision-making and sensing infrastructures.

Purpose of the Study:

To introduce the concept of interdependent networks in smart cities.
To address the challenge of developing data analytics for interdependent decision-making.
To explore agent-based distributed decision-making solutions.

Main Methods:

Providing a conceptual overview of real-world interdependent networks.
Outlining data science challenges and solutions for network coupling.
Discussing communication reliability among intelligent agents.

Main Results:

Identified the growing coupling among networks in smart city contexts.
Proposed agent-based distributed decision-making as a key solution.
Highlighted challenges in ensuring reliable inter-network communication.

Conclusions:

Interdependent networks are crucial for smart city infrastructures.
Data science approaches are vital for enabling interdependent decision-making.
Future research should focus on the intersection of network and data science for intelligent agents.