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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...
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)...
Clearance Models: Noncompartmental Models01:17

Clearance Models: Noncompartmental Models

Clearance is a pharmacokinetic parameter traditionally defined by compartment models, signifying the rate at which a drug is expelled from the body. However, a noncompartmental model offers an alternative method for assessing clearance, primarily employing empirical data obtained after administering a single drug dose.
The noncompartmental approach capitalizes on extensive sampling data, correlating the volume of distribution to systemic exposure and the administered dosage. This method enables...
Criticisms of the Evolutionary Perspective01:23

Criticisms of the Evolutionary Perspective

In a study where individuals posing as strangers offered compliments and proposed casual sex to students, the responses differed significantly based on gender. Not a single woman accepted the proposal, while 70% of the men agreed. This outcome provides a useful scenario to explore through the lens of evolutionary psychology and social learning theory, highlighting the diverse perspectives on human sexual behaviors.
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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...
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...

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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

A Bayesian perspective on a non-parsimonious parsimony model.

John P Huelsenbeck1, Cécile Ané, Bret Larget

  • 1Department of Integrative Biology, University of California, Berkeley, CA 94720-3140, USA. johnh@berkeley.edu

Systematic Biology
|June 24, 2008
PubMed
Summary

We developed a Bayesian approach for phylogenetic tree analysis using the no-common-mechanism model. This method efficiently estimates clade probabilities for large datasets, improving phylogenetic inference.

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A Practical Guide to Phylogenetics for Nonexperts
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A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

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A Practical Guide to Phylogenetics for Nonexperts
12:00

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Area of Science:

  • Computational Biology
  • Phylogenetics
  • Statistical Modeling

Background:

  • Maximum likelihood and maximum parsimony methods in phylogenetics can correspond under certain stochastic models.
  • The no-common-mechanism model, with independent branch lengths per site, presents a high-dimensional parameter space.
  • Scaling challenges arise with increasing alignment length in this model.

Purpose of the Study:

  • To apply a Bayesian approach to the no-common-mechanism model for phylogenetic inference.
  • To develop an efficient computational method for exploring phylogenetic tree space.
  • To estimate posterior probabilities of clades in large phylogenetic trees.

Main Methods:

  • Implemented a Bayesian framework with independent gamma priors on branch-length parameters.
  • Analytically integrated over branch lengths to simplify computation.
  • Developed an efficient Markov chain Monte Carlo (MCMC) method for phylogenetic tree exploration.

Main Results:

  • Successfully estimated posterior clade probabilities for trees with up to 500 sequences.
  • Found that the integrated likelihood approximates a rescaled parsimony score.
  • Observed dependence of branch length posterior distributions on maximum parsimony reconstructions.

Conclusions:

  • The Bayesian approach offers an efficient method for phylogenetic analysis, particularly for morphological data.
  • The method enhances the performance of MCMC for models with shared branch-length parameters.
  • Further research is needed to fully address branch-length parameter behavior in this model.