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

Transmission-Line Differential Equations01:26

Transmission-Line Differential Equations

1.1K
Transmission lines are essential components of electrical power systems. They are characterized by the distributed nature of resistance (R), inductance (L), and capacitance (C) per unit length. To analyze these lines, differential equations are employed to model the variations in voltage and current along the line.
Line Section Model
A circuit representing a line section of length Δx helps in understanding the transmission line parameters. The voltage V(x) and current i(x) are measured from...
1.1K
Equivalent Resistance01:16

Equivalent Resistance

1.2K
In circuit analysis, situations often arise where resistors are neither in series nor parallel configurations. To tackle such scenarios, three-terminal equivalent networks like the wye (Y) (Figure 1 (a)) or tee (T) and delta (Δ) (Figure 1 (b)) or pi (π) networks come into play. These networks offer versatile solutions and are frequently encountered in various applications, including three-phase electrical systems, electrical filters, and matching networks.
1.2K
Differential Relays01:20

Differential Relays

919
Differential relays are used to protect generators, buses, and transformers by comparing electrical quantities at different points. When a fault occurs, the difference in current between the two points triggers the relay to operate, opening the circuit breaker. Under normal conditions, the current entering (i1) and leaving (i2) a generator are equal. When a fault occurs, however, these currents become unequal, and the difference current flows in the relay operating coil, causing the relay to...
919
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

1.5K
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
1.5K
Modeling with Differential Equations01:25

Modeling with Differential Equations

272
Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
272
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

435
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
435

You might also read

Related Articles

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

Sort by
Same author

Minimax and adaptive transfer learning for nonparametric classification under distributed differential privacy constraints.

Journal of the Royal Statistical Society. Series B, Statistical methodology·2026
Same author

The Sepsis ImmunoScore Predicts Sepsis, Mortality, and Deterioration Better than Clinical Scores and Widely Available Biomarkers.

Diagnostics (Basel, Switzerland)·2026
Same author

Simultaneous Immunofluorescence-Based In Situ mRNA Expression and Protein Detection in Bone Marrow Biopsy Samples.

Bio-protocol·2026
Same author

Robust causal gene network estimation for large-scale single-cell perturbation screens using reduced control function.

bioRxiv : the preprint server for biology·2026
Same author

Development of an automated, imaging-based preoperative screening model for early identification of malnutrition in an abdominal surgery cohort.

medRxiv : the preprint server for health sciences·2026
Same author

Factors Associated with Adherence to Recommended Colorectal Surveillance Intervals in Lynch Syndrome.

Cancers·2026

Related Experiment Video

Updated: Apr 11, 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

Direct estimation of differential networks.

Sihai Dave Zhao1, T Tony Cai2, Hongzhe Li1

  • 1Department of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania 19104, USA.

Biometrika
|May 30, 2015
PubMed
Summary

This study introduces a novel method for estimating differential genetic networks by directly modeling the difference between precision matrices. This approach accurately identifies network changes and outperforms existing methods in simulations and real-world data analysis.

Keywords:
Differential networkGraphical modelHigh dimensionalityPrecision matrix

More Related Videos

Rapid Development of Cell State Identification Circuits with Poly-Transfection
09:21

Rapid Development of Cell State Identification Circuits with Poly-Transfection

Published on: February 24, 2023

2.1K
Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

3.5K

Related Experiment Videos

Last Updated: Apr 11, 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
Rapid Development of Cell State Identification Circuits with Poly-Transfection
09:21

Rapid Development of Cell State Identification Circuits with Poly-Transfection

Published on: February 24, 2023

2.1K
Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

3.5K

Area of Science:

  • Computational Biology
  • Genomics
  • Network Analysis

Background:

  • Understanding genetic network differences between conditions is crucial.
  • Existing methods for network comparison have limitations, such as requiring sparsity.

Purpose of the Study:

  • To propose a direct method for estimating the difference between precision matrices of genetic networks.
  • To develop an approach that does not require individual network sparsity, allowing for hub nodes.

Main Methods:

  • Modeling condition-specific networks using precision matrices of multivariate normal random vectors.
  • Directly estimating the difference between these precision matrices.
  • Assuming sparsity in the true differential network for theoretical guarantees.

Main Results:

  • The proposed direct estimation method is consistent in support recovery and estimation.
  • Simulations show superior performance compared to existing methods.
  • The method's utility is demonstrated on ovarian cancer gene expression data.

Conclusions:

  • Direct estimation of differential networks offers an effective alternative to separate or joint estimation.
  • The method accommodates complex network structures, including those with hub nodes.
  • This approach provides valuable insights into condition-specific genetic network alterations.