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 Experiment Video

Updated: Jun 29, 2025

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

1.1K

Comparing Causal Bayesian Networks Estimated from Data.

Sisi Ma1, Roshan Tourani1

  • 1Institute for Health Informatics, University of Minnesota, Minneapolis, MN 55455, USA.

Entropy (Basel, Switzerland)
|March 28, 2024
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

Causality in Epidemiology01:21

Causality in Epidemiology

407
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
407
Correlation of Experimental Data01:23

Correlation of Experimental Data

230
Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity,...
230
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

69
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...
69
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

128
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
128
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

181
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
181
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

490
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...
490

You might also read

Related Articles

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

Sort by
Same author

Biological and Social Risk Factors for Predicting Type 2 Diabetes in Youth with Prediabetes: Review of Existing Prediction Models.

Diabetes therapy : research, treatment and education of diabetes and related disorders·2026
Same author

Managing Exercise-Related Glycemic Events in Type 1 Diabetes: Development and Validation of Predictive Models for a Practical Decision Support Tool.

JMIR diabetes·2025
Same author

The Rapid Evolution of Continuous Blood Pressure Measurement: Future Considerations.

American journal of hypertension·2025
Same author

Consensus modeling: Safer transfer learning for small health systems.

Artificial intelligence in medicine·2024
Same author

Not all public gatherings are the same: a comparative analysis of COVID superspreader events.

Journal of public health (Oxford, England)·2022
Same author

Causal analysis identifies small HDL particles and physical activity as key determinants of longevity of older adults.

EBioMedicine·2022
Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026
Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026
Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026
Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026
Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026
Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026
See all related articles

Comparing causal mechanisms across systems requires advanced methods. New resampling techniques improve accuracy, especially when data sample sizes vary, enabling better insights into commonalities and differences.

Area of Science:

  • Computational biology
  • Systems biology
  • Network analysis

Background:

  • Understanding causal mechanisms within a single system is often insufficient for complex questions.
  • Comparing causal mechanisms across multiple systems reveals consistent and distinct relationships, aiding discoveries like drug repurposing.
  • Estimating causal mechanisms from data is challenging, especially when comparing systems with differing data characteristics.

Purpose of the Study:

  • To address the limitations of naive methods for comparing causal mechanisms across systems with heterogeneous data.
  • To introduce and evaluate novel resampling-based methods for more accurate estimation of differences between causal networks.
  • To improve the reliability of identifying shared and unique causal relationships among multiple systems.

Main Methods:

Keywords:
causal Bayesian networkcausal discoveryresamplinguncertainty

More Related Videos

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.3K
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

2.1K

Related Experiment Videos

Last Updated: Jun 29, 2025

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

1.1K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.3K
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

2.1K
  • Developed bootstrap estimation and equal sample size resampling methods for causal network comparison.
  • Utilized systematic simulations with diverse network structures and sample sizes to test performance.
  • Evaluated methods on real-world biomedical datasets with varied data designs.

Main Results:

  • Naive methods for comparing causal networks can yield sub-optimal results, particularly with unequal sample sizes.
  • Resampling methods (bootstrap and equal sample size) demonstrate improved accuracy and confidence in estimating causal network differences.
  • The proposed methods show robust performance across simulated and real-world biomedical data.

Conclusions:

  • Comparing causal mechanisms across multiple systems necessitates advanced estimation techniques beyond naive approaches.
  • Bootstrap and equal sample size resampling provide more reliable methods for inferring similarities and differences in causal networks.
  • These enhanced methods offer valuable tools for biological discovery, such as identifying common disease mechanisms.