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

Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism
Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism
Pharmacodynamic Models: Additive and Proportional Drug Effect Model01:09

Pharmacodynamic Models: Additive and Proportional Drug Effect Model

Drug response models describe how pharmacological agents interact with biological systems to produce measurable effects. Baseline responses are inherent physiological activities without a drug significantly influencing the observed pharmacological outcomes. Depending on the drug response model employed, these baseline responses may combine with the drug's effect in either an additive or proportional manner.Additive Drug Response ModelIn the additive model, the drug effect is independent of the...
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
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...

You might also read

Related Articles

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

Sort by
Same author

Individual identification of dairy cows with occluded camera views using open-set contrastive learning model.

Journal of dairy science·2026
Same author

Real-time milk traits and wearable sensor-derived rumination and feeding behaviors for assessing heat stress effects in dairy cattle.

Journal of dairy science·2026
Same author

Editorial: Insights in livestock genomics.

Frontiers in genetics·2026
Same author

Corrigendum to "Handling errors in the response: Considerations for leveraging unsupervised or incomplete data for genetic evaluations" (JDS Commun. 5:675-680).

JDS communications·2026
Same author

Cattle grow taller: Implications of outdated ordinal scores for genetic evaluations and selection?

JDS communications·2026
Same author

Cross-validation strategies under data dependency: An example with anemia prediction in sheep using ocular conjunctiva images.

Preventive veterinary medicine·2026

Related Experiment Video

Updated: Jun 17, 2026

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
09:35

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

Bayesian model averaging for evaluation of candidate gene effects.

Xiao-Lin Wu1, Daniel Gianola, Guilherme J M Rosa

  • 1Department of Dairy Science, University of Wisconsin, Madison, WI 53706, USA. nick.wu@ansci.wisc.edu

Genetica
|January 6, 2010
PubMed
Summary

This study introduces Bayesian model averaging to evaluate candidate gene effects, accounting for uncertainty in statistical model selection. This approach improves the reliability of genetic association studies by considering multiple models simultaneously.

More Related Videos

In Vivo Modeling of the Morbid Human Genome using Danio rerio
12:31

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Related Experiment Videos

Last Updated: Jun 17, 2026

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
09:35

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

In Vivo Modeling of the Morbid Human Genome using Danio rerio
12:31

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Assessing candidate gene effects involves variable selection and model comparison.
  • Multiple statistical models can fit genetic data, leading to uncertainty in model choice.
  • Inference based on a single model overlooks potential alternative explanations.

Purpose of the Study:

  • To propose a Bayesian model averaging (BMA) approach for evaluating candidate gene effects.
  • To incorporate model uncertainty into statistical inference for genetic association studies.
  • To demonstrate the utility of BMA using simulated genetic data.

Main Methods:

  • Utilizing Bayesian model averaging (BMA) for statistical assessment of candidate gene effects.
  • Implementing BMA through simultaneous sampling of multiple competing models.
  • Applying the method to a simulated dataset with ten candidate genes.

Main Results:

  • The proposed BMA approach effectively incorporates model uncertainty into candidate gene effect evaluation.
  • Simulations demonstrated the practical application and features of the BMA method.
  • The method provides a more robust inference framework compared to single-model approaches.

Conclusions:

  • Bayesian model averaging offers a superior framework for robust inference of candidate gene effects.
  • Addressing model uncertainty is crucial for reliable genetic association studies.
  • The proposed BMA method enhances the evaluation of genetic influences in complex traits.