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

Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

121
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.
121
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

94
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...
94
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

642
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...
642
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

74
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...
74
Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

2.6K
The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an...
2.6K
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

2.6K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
2.6K

You might also read

Related Articles

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

Sort by
Same author

Shape-constrained, changepoint additive models for time series omics data with cpam.

Nucleic acids research·2026
Same author

Convergence-Divergence Models: Generalizations of Phylogenetic Trees Modeling Gene Flow Over Time.

Bulletin of mathematical biology·2025
Same author

From Trees to Traits: A Review of Advances in PhyloG2P Methods and Future Directions.

Genome biology and evolution·2025
Same author

Leveraging advances in machine learning for the robust classification and interpretation of networks.

Royal Society open science·2025
Same author

Multi-response phylogenetic mixed models: concepts and application.

Biological reviews of the Cambridge Philosophical Society·2025
Same author

Dehydration rapidly induces expression of NCED genes from a single subclade in diverse eudicots.

Planta·2025

Related Experiment Video

Updated: Aug 16, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K

Performance of Akaike Information Criterion and Bayesian Information Criterion in Selecting Partition Models and

Qin Liu1, Michael A Charleston1, Shane A Richards1

  • 1School of Natural Sciences, University of Tasmania, Hobart, TAS, Australia.

Systematic Biology
|December 28, 2022
PubMed
Summary
This summary is machine-generated.

When comparing complex evolutionary models in phylogenetics, the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) can be unreliable. Researchers should exercise caution with AIC and BIC for model selection, especially with genomic data heterogeneity.

More Related Videos

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.6K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.0K

Related Experiment Videos

Last Updated: Aug 16, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.6K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.0K

Area of Science:

  • Molecular Phylogenetics
  • Computational Biology
  • Genomic Data Analysis

Background:

  • Partition and mixture models are used to analyze heterogeneity in genomic sequencing data, offering better fits than homogeneous models.
  • The Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) are common for model selection in phylogenetics.
  • Previous research indicated issues with using AIC for comparing partition and mixture models.

Purpose of the Study:

  • To clarify why AIC struggles with comparing mixture and partition models, beyond just misestimating Kullback-Leibler divergence.
  • To investigate the performance of AIC and BIC when comparing mixture models amongst themselves and partition models amongst themselves.
  • To assess the impact of mispartitioning on model performance and selection.

Main Methods:

  • Evaluated the performance of AIC and BIC in model selection under nonstandard conditions (e.g., short evolutionary edges).
  • Compared the accuracy of parameter estimation (edge lengths, base frequencies, substitution rates) for models selected by AIC and BIC.
  • Simulated data with varying degrees of mispartitioning to test the robustness of partition models and model selection criteria.

Main Results:

  • Under nonstandard conditions, AIC underestimates Kullback-Leibler divergence, favoring complex mixture models, while BIC favors simpler ones.
  • AIC-selected mixture models better estimated edge lengths, whereas BIC-selected models better estimated base frequencies and substitution rates.
  • Both AIC and BIC favored simpler partition models over more complex ones, even when the complex model generated the data. Mispartitioning reduced the accuracy of parameter estimates from partition models.

Conclusions:

  • AIC and BIC can be unreliable for selecting between partition and mixture models, particularly under nonstandard evolutionary conditions or with mispartitioned data.
  • Researchers should be cautious when using AIC and BIC for model selection in phylogenetics.
  • Alternative model selection methods like cross-validation and bootstrapping may be necessary, though they might have similar limitations.