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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)...
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...
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,...
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
Polygenic Traits01:18

Polygenic Traits

When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...

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Updated: Jun 25, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

Bayesian finite Markov mixture model for temporal multi-tissue polygenic patterns.

Yulan Liang1, Arpad Kelemen

  • 1Department of Organizational Systems and Adult Health, University of Maryland, Baltimore, 21201, USA. ylian001@umaryland.edu

Biometrical Journal. Biometrische Zeitschrift
|February 7, 2009
PubMed
Summary
This summary is machine-generated.

Finite mixture models reveal behavioral patterns in gene expression data. A Bayesian Finite Markov Mixture Model identifies differentially expressed genes and dynamic clusters in complex temporal datasets.

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Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease
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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease
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Published on: September 20, 2024

Area of Science:

  • * Computational biology and bioinformatics.
  • * Statistical modeling for high-dimensional biological data.

Background:

  • * Gene expression data exhibits heterogeneity and complex temporal dynamics.
  • * Understanding behavioral patterns and systemic responses requires advanced analytical methods.

Purpose of the Study:

  • * To develop a Bayesian Finite Markov Mixture Model for analyzing time-course gene expression data.
  • * To identify differentially expressed genes and dynamic clusters in multi-tissue datasets.

Main Methods:

  • * Application of Bayesian Finite Markov Mixture Model with a Dirichlet Prior.
  • * Utilizing the Deviance Information Criterion for model selection.
  • * Comparison with the CAGED Bayesian clustering method.

Main Results:

  • * The proposed model effectively captures dynamic changes and patterns in complex temporal gene expression data.
  • * Successful identification of differentially expressed genes and temporal clusters.
  • * Demonstrated ability to handle high-dimensional, multi-tissue data.

Conclusions:

  • * Finite mixture models offer valuable insights into gene expression heterogeneity.
  • * The Bayesian Finite Markov Mixture Model provides a robust framework for analyzing dynamic biological processes.
  • * The developed model excels in capturing complex temporal patterns in gene expression data.