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

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
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

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...
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...

You might also read

Related Articles

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

Sort by
Same author

REFORMS: Consensus-based Recommendations for Machine-learning-based Science.

Science advances·2024
Same author

An illusion of predictability in scientific results: Even experts confuse inferential uncertainty and outcome variability.

Proceedings of the National Academy of Sciences of the United States of America·2023
Same author

A Generative Modeling Approach to Calibrated Predictions: A Use Case on Menstrual Cycle Length Prediction.

Proceedings of machine learning research·2022
Same author

A predictive model for next cycle start date that accounts for adherence in menstrual self-tracking.

Journal of the American Medical Informatics Association : JAMIA·2021
Same author

Integrating explanation and prediction in computational social science.

Nature·2021
Same author

Dose-response modeling in high-throughput cancer drug screenings: an end-to-end approach.

Biostatistics (Oxford, England)·2021
Same journal

Erratum: Bacterial Turbulence at Compressible Fluid Interfaces [Phys. Rev. Lett. 136, 138301 (2026)].

Physical review letters·2026
Same journal

Unveiling Light-Quark Yukawa Flavor Structure via Dihadron Fragmentation at Lepton Colliders.

Physical review letters·2026
Same journal

Adaptable Route to Fast Coherent State Transport via Bang-Bang-Bang Protocols.

Physical review letters·2026
Same journal

Topological Transition and Emergence of Elasticity of Dislocation in Skyrmion Lattice: Beyond Kittel's Magnetic-Polar Analogy.

Physical review letters·2026
Same journal

Pound-Drever-Hall Method for Superconducting-Qubit Readout.

Physical review letters·2026
Same journal

Coupling a ^{73}Ge Nuclear Spin to an Electrostatically Defined Quantum Dot in Silicon.

Physical review letters·2026
See all related articles

Related Experiment Video

Updated: Jul 3, 2026

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

Bayesian approach to network modularity.

Jake M Hofman1, Chris H Wiggins

  • 1Department of Physics, Columbia University, New York, New York 10027, USA. jmh2045@columbia.edu

Physical Review Letters
|July 23, 2008
PubMed
Summary
This summary is machine-generated.

We developed a new Bayesian technique for network module detection. This method efficiently identifies the optimal number of modules and overcomes common resolution limits in network analysis.

More Related Videos

Two-Photon Polymerization 3D-Printing of Micro-scale Neuronal Cell Culture Devices
07:38

Two-Photon Polymerization 3D-Printing of Micro-scale Neuronal Cell Culture Devices

Published on: June 7, 2024

Related Experiment Videos

Last Updated: Jul 3, 2026

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

Two-Photon Polymerization 3D-Printing of Micro-scale Neuronal Cell Culture Devices
07:38

Two-Photon Polymerization 3D-Printing of Micro-scale Neuronal Cell Culture Devices

Published on: June 7, 2024

Area of Science:

  • Network science
  • Computational biology
  • Statistical modeling

Background:

  • Module detection is crucial for understanding complex networks.
  • Existing methods face limitations, including the resolution limit problem.
  • A need exists for efficient, interpretable, and accurate module inference techniques.

Purpose of the Study:

  • To present an efficient, principled, and interpretable technique for network module inference.
  • To identify the optimal number of modules within a given network.
  • To demonstrate the method's ability to overcome the resolution limit problem.

Main Methods:

  • Utilizing Bayesian methods for model selection, a long-established statistical approach.
  • Implementing a recent variational technique for efficient computation.
  • Applying the developed method to both synthetic and real-world network data.

Main Results:

  • The proposed technique accurately infers module assignments and the optimal number of modules.
  • Several existing module-finding methods are shown to be special cases of this approach.
  • The method successfully overcomes the resolution limit problem, recovering true module counts.

Conclusions:

  • The presented Bayesian variational approach offers an efficient and accurate solution for network module detection.
  • This technique provides a unified framework for understanding various module-finding algorithms.
  • The method's interpretability and ability to handle model selection make it broadly applicable.