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

Network Function of a Circuit01:25

Network Function of a Circuit

338
Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
338
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

137
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
137
Block Diagram Reduction01:22

Block Diagram Reduction

259
The process of deriving the transfer function of a control system often involves reducing its block diagram to a single block. This simplification can be achieved through a series of strategic operations, including relocating branch points and comparators. These operations preserve the overall function of the system while allowing for easier manipulation and combination of blocks.
The first step in this process is the identification and relocation of a branch point. A branch point, where a...
259
Multimachine Stability01:25

Multimachine Stability

210
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
210
Bus Impedance Matrix01:24

Bus Impedance Matrix

153
Calculating subtransient fault currents for three-phase faults in an N-bus power system involves using the positive-sequence network. When a three-phase short circuit occurs at a specific bus, the analysis uses the superposition method to evaluate two separate circuits.
In the first circuit, all machine voltage sources are short-circuited, leaving only the prefault voltage source at the fault location. The positive-sequence bus impedance matrix can be determined by solving the nodal equations,...
153
Circuit Terminology01:14

Circuit Terminology

1.8K
An electrical network is a system composed of interconnected elements, such as resistors, capacitors, inductors, and voltage or current sources. Unlike a circuit, an electrical network does not necessarily form a closed path. In other words, while all circuits can be considered networks due to their interconnected nature, not every network qualifies as a circuit.
A circuit, on the other hand, is also an interconnected system of electrical elements but must contain one or more closed paths.
1.8K

You might also read

Related Articles

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

Sort by
Same author

Identifying driver modules based on multi-omics biological networks in prostate cancer.

IET systems biology·2022
Same author

Integrating multi-omics data to identify dysregulated modules in endometrial cancer.

Briefings in functional genomics·2022
Same author

JAMIE: A software tool for jointly analyzing multiple ChIP-chip experiments.

Methods in molecular biology (Clifton, N.J.)·2011
Same author

Morphine-induced conditioned place preference in mice: metabolomic profiling of brain tissue to find "molecular switch" of drug abuse by gas chromatography/mass spectrometry.

Analytica chimica acta·2011
Same author

[The interventions effect-assessment of the workers exposed to N, N-dimethylformamide by percutaneous in a synthetic leather factory].

Zhonghua lao dong wei sheng zhi ye bing za zhi = Zhonghua laodong weisheng zhiyebing zazhi = Chinese journal of industrial hygiene and occupational diseases·2011
Same author

[The analysis of effect of Th1/Th2 cytokine in the different prognosis in severe influenza A (H1N1)].

Zhonghua shi yan he lin chuang bing du xue za zhi = Zhonghua shiyan he linchuang bingduxue zazhi = Chinese journal of experimental and clinical virology·2011

Related Experiment Video

Updated: Aug 7, 2025

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
09:49

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

Published on: September 25, 2021

4.4K

MultiSimNeNc: A network representation learning-based module identification method by network embedding and

Hao Wu1, Biting Liang2, Zhongli Chen3

  • 1College of Information Engineering, Northwest A&F University, 712100, Yangling, China; School of Software, Shandong University, 250100, Jinan, China.

Computers in Biology and Medicine
|March 8, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces MultiSimNeNc, a new network-based method for identifying gene modules. It improves cancer research by accurately detecting gene patterns using network representation learning and clustering.

Keywords:
GlioblastomaGraph convolutionModule identificationNetwork representation learning

More Related Videos

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.7K
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: Aug 7, 2025

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
09:49

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

Published on: September 25, 2021

4.4K
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.7K
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
  • Network Science
  • Bioinformatics

Background:

  • Gene module identification is crucial for understanding cancer. Existing graph clustering methods often lack accuracy due to limited consideration of network topology.
  • Multi-omics data integration offers a comprehensive view for biological network construction.

Purpose of the Study:

  • To develop a novel network-based method, MultiSimNeNc, for accurate gene module identification.
  • To improve the understanding of cancer's biomolecular mechanisms at the module level.

Main Methods:

  • Integrating network representation learning (NRL) with clustering algorithms.
  • Utilizing graph convolution (GC) for multi-order network similarity and non-negative matrix factorization (NMF) for node characterization.
  • Employing Bayesian Information Criterion (BIC) for module number prediction and Gaussian Mixture Model (GMM) for module identification.

Main Results:

  • MultiSimNeNc demonstrated superior performance compared to state-of-the-art algorithms in module identification accuracy.
  • The method was validated on biological networks derived from glioblastoma multi-omics data and benchmark networks.

Conclusions:

  • MultiSimNeNc is an effective approach for identifying gene modules in biological networks.
  • The method enhances the understanding of pathogenesis by providing accurate module-level insights.