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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)...
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...
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...
Contingency Table01:29

Contingency Table

A contingency table provides a way of portraying data that can facilitate calculating probabilities. It is a method of displaying a frequency distribution as a table with rows and columns to show how two variables may be dependent (contingent) upon each other; The table helps determine conditional probabilities quite quickly and can help systematically organize, analyze and quantify data. The table displays sample values concerning two variables that may be dependent or contingent on one...

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

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Published on: December 10, 2012

A latent class model with hidden Markov dependence for array CGH data.

Stacia M DeSantis1, E Andrés Houseman, Brent A Coull

  • 1Department of Biostatistics, Bioinformatics, and Epidemiology, Medical University of South Carolina, 135 Cannon Street, Suite 303, Charleston, South Carolina 29403, USA. sdesanti@hsph.harvard.edu

Biometrics
|April 29, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a supervised Bayesian method using hidden Markov models to classify tumors based on array comparative genomic hybridization (aCGH) profiles. The approach effectively identifies distinct tumor subsets with different genomic alterations and improves survival prediction.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Array comparative genomic hybridization (aCGH) is crucial for detecting cancer-related genomic alterations.
  • aCGH generates fluorescence ratios indicating DNA copy number changes between tumor and healthy cells.
  • Analyzing complex aCGH data is challenging due to numerous correlated measures.

Purpose of the Study:

  • To develop a supervised Bayesian latent class approach for tumor classification using aCGH data.
  • To account for dependencies in intensity ratios using a hidden Markov model.
  • To guide classification using clinical endpoints for improved biological relevance.

Main Methods:

  • Developed a supervised Bayesian latent class model.
  • Incorporated a hidden Markov model to handle correlated intensity ratios.
  • Performed posterior inference on class-specific copy number gains and losses.

Main Results:

  • Successfully identified distinct subsets of brain tumors with varying genomic profiles.
  • Demonstrated superior differentiation of tumor classes by survival compared to unsupervised methods.
  • The supervised approach effectively links genomic alterations to clinical outcomes.

Conclusions:

  • The developed supervised Bayesian latent class model offers a robust method for aCGH data analysis in cancer research.
  • This technique enhances the ability to classify tumors based on genomic profiles and predict patient survival.
  • The approach provides valuable insights into the genomic basis of tumor heterogeneity and progression.