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,...

You might also read

Related Articles

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

Sort by

Same author

Reward expectation and receipt differentially modulate the spiking of accumbens D1+ and D2+ neurons.

Current biology : CB·2025

Same author

Abstracts of presentations to the Annual Meetings of the Canadian Society of Colon and Rectal Surgeons Canadian Association of General Surgeons Canadian Association of Thoracic Surgeons: Canadian Surgery Forum, Toronto, Ont., September 6-9, 2007.

Canadian journal of surgery. Journal canadien de chirurgie·2023

Same author

Understanding spatial and temporal patterning of astrocyte calcium transients via interactions between network transport and extracellular diffusion.

Physical biology·2016

Same author

Sex dependent alterations in mitochondrial electron transport chain proteins following neonatal rat cerebral hypoxic-ischemia.

Journal of bioenergetics and biomembranes·2016

Same author

Sex-dependent mitophagy and neuronal death following rat neonatal hypoxia-ischemia.

Neuroscience·2016

Same author

Proximal femoral fractures: the quest to improve outcomes.

The bone & joint journal·2015

Same journal

Tension on dsDNA bound to ssDNA-RecA filaments may play an important role in driving efficient and accurate homology recognition and strand exchange.

Physical review. E, Statistical, nonlinear, and soft matter physics·2016

Same journal

Publisher's Note: Amplitude-phase coupling drives chimera states in globally coupled laser networks [Phys. Rev. E 91, 040901(R) (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016

Same journal

Erratum: Shapes of sedimenting soft elastic capsules in a viscous fluid [Phys. Rev. E 92, 033003 (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016

Same journal

Erratum: Attenuation of excitation decay rate due to collective effect [Phys. Rev. E 90, 022142 (2014)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016

Same journal

Publisher's Note: Role of connectivity and fluctuations in the nucleation of calcium waves in cardiac cells [Phys. Rev. E 92, 052715 (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016

Same journal

Publisher's Note: Lattice Boltzmann approach for complex nonequilibrium flows [Phys. Rev. E 92, 043308 (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 22, 2026

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

Functional clustering algorithm for the analysis of dynamic network data.

S Feldt¹, J Waddell, V L Hetrick

¹Department of Physics, University of Michigan, Ann Arbor, Michigan 48109, USA. sarahfel@umich.edu

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics

|June 13, 2009

Summary

This summary is machine-generated.

We developed a new method to find functional groups in event data without needing to know the group number beforehand. This technique improves upon existing methods for analyzing neural data and understanding brain activity.

More Related Videos

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

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: Jun 22, 2026

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

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

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:

Computational Neuroscience
Data Analysis
Systems Neuroscience

Background:

Analyzing complex discrete event data, such as neural spike trains, is challenging.
Identifying functional clusters requires prior knowledge of the number of groups, limiting flexibility.
Existing methods may not optimally detect changes in network dynamics.

Purpose of the Study:

To develop a novel algorithm for detecting functional clusters in discrete event data.
To enable clustering without prior knowledge of the number of functional groups.
To demonstrate the algorithm's effectiveness in analyzing neural data and identifying state-dependent patterns.

Main Methods:

A technique for detecting functional clusters in discrete event data was formulated.
The algorithm progressively combines data traces and uses surrogate data sets to determine the optimal clustering cutoff.
Applied to simulated neural spike train data and real neural data from the mouse hippocampus.

Main Results:

The algorithm successfully detected functional clusters without prior knowledge of group numbers.
Performance on simulated data surpassed existing methods.
Observed state-dependent clustering patterns in hippocampal data during exploration and sleep, aligning with neurophysiological processes.

Conclusions:

The developed algorithm provides an effective and intuitive method for functional cluster detection in discrete event data.
It offers improved performance over existing techniques, particularly for neural data analysis.
The findings support the algorithm's utility in uncovering neurophysiological insights related to memory consolidation and brain states.