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Related Concept Videos

Variability: Analysis01:11

Variability: Analysis

Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
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-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This relationship...
Pharmacogenetics of Drug Metabolism: Overview01:27

Pharmacogenetics of Drug Metabolism: Overview

Genetic polymorphism in drug metabolism is crucial to the inter-individual variability observed in drug responses. Drug metabolism primarily involves the chemical modification of drugs and other xenobiotics to enhance their elimination by increasing their polarity. Two main classes of enzymes mediate this biotransformation process: Phase I enzymes, primarily cytochrome P450s, catalyze oxidation and reduction reactions, while other enzymes, such as esterases, mediate hydrolysis, and Phase II...
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
Genetic Variation01:25

Genetic Variation

Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
Genes exist in different versions called alleles, which...

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Related Experiment Video

Updated: May 20, 2026

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS)
07:34

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS)

Published on: March 14, 2013

MetaboVariation 2.0: Multivariate analysis for identifying metabolite variation at the individual level.

Shubbham Gupta1,2, Isobel Claire Gormley2, Lorraine Brennan1

  • 1School of Agriculture and Food Science, University College Dublin, Dublin, Ireland.

Plos One
|May 18, 2026
PubMed
Summary
This summary is machine-generated.

MetaboVariation 2.0 is a new multivariate Bayesian model that identifies individual metabolic variations by considering metabolite dependencies. This advanced approach improves the assessment of individual metabolic profiles in metabolomics research.

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Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS)
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Published on: March 14, 2013

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08:27

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Area of Science:

  • Metabolomics
  • Systems Biology
  • Biostatistics

Background:

  • Individual metabolic profiles are influenced by genetics, environment, and lifestyle.
  • Detecting intra-individual metabolic variations is crucial for personalized health insights.
  • Previous univariate methods did not fully capture complex metabolite interdependencies.

Purpose of the Study:

  • Introduce MetaboVariation 2.0, a multivariate Bayesian generalized linear model.
  • To accurately flag individuals with significant intra-individual variations in metabolite levels across repeated measurements.
  • To improve upon univariate approaches by incorporating metabolite dependencies.

Main Methods:

  • Developed a multivariate Bayesian generalized linear model (MetaboVariation 2.0).
  • Incorporated dependencies between multiple metabolites for a comprehensive analysis.
  • Validated performance through simulation studies and application to a real metabolomics dataset.

Main Results:

  • MetaboVariation 2.0 outperformed the univariate predecessor, especially with positive metabolite correlations.
  • The model demonstrated improved accuracy in capturing metabolic dependencies.
  • Applied to plasma data, it identified intra-individual variations in 15.2% of individuals.

Conclusions:

  • MetaboVariation 2.0 provides a more complete view of individual metabolic profiles by accounting for metabolite interdependencies.
  • This multivariate approach represents a significant advancement in assessing individual-level metabolite variation.
  • A software implementation (MetaboVariation R package) is available for wider research use.