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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...
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...
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

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).
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
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,...
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

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, comparing...

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Related Experiment Video

Updated: Jun 19, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

A dirichlet process covarion mixture model and its assessments using posterior predictive discrepancy tests.

Yan Zhou1, Henner Brinkmann, Nicolas Rodrigue

  • 1Département de Biochimie, Centre Robert-Cedergren, Université de Montréal, Succursale Centre-Ville, Québec, Canada.

Molecular Biology and Evolution
|October 14, 2009
PubMed
Summary

Heterotachy, or varying substitution rates, can skew phylogenetic analyses. A new covarion mixture (CM) model reveals these rates significantly differ across sites, improving evolutionary inference.

Related Experiment Videos

Last Updated: Jun 19, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Area of Science:

  • Computational Biology
  • Phylogenetics
  • Evolutionary Biology

Background:

  • Heterotachy, the variation of substitution rates at a site over time, is common in sequence alignments.
  • This phenomenon can mislead phylogenetic inferences based on probabilistic models.
  • The covarion model, a form of heterotachy, assumes sites switch between 'ON' and 'OFF' substitution states, with homogeneous switch rates across sites.

Purpose of the Study:

  • To develop and test a novel model addressing the homogeneity assumption of covarion switch rates.
  • To investigate site-specific variation in covarion parameters.
  • To improve the accuracy of phylogenetic inferences by accounting for complex evolutionary processes.

Main Methods:

  • Developed the covarion mixture (CM) model using an infinite mixture approach with a Dirichlet process prior.
  • Integrated the CM model with the CAT model (for amino acid frequency heterogeneity) and a gamma distribution (for rate heterogeneity).
  • Applied the combined model to large sequence alignments and used posterior predictive discrepancy tests for model evaluation.

Main Results:

  • Demonstrated significant heterogeneity of covarion parameters across sites in empirical datasets.
  • The CM model, incorporating site-specific variation, provided a better fit than models assuming homogeneous parameters.
  • Posterior predictive discrepancy tests highlighted the importance of modeling site-specific heterogeneities.

Conclusions:

  • The homogeneity assumption in standard covarion models is often violated.
  • The covarion mixture (CM) model effectively captures site-specific variation in substitution dynamics.
  • Accounting for heterotachy and site-specific rate variation is crucial for accurate phylogenetic reconstruction.