Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Systems of Linear Equations in Two Variables01:25

Systems of Linear Equations in Two Variables

310
Solving a system of linear equations is a fundamental concept in algebra. A system of equations consists of two or more linear equations involving the same set of variables. One of the most efficient algebraic methods for solving such systems is the substitution method. This technique involves expressing one variable in terms of the other from one equation and substituting it into the second equation. This method is particularly useful when one of the equations is easily rearranged.Consider the...
310
Application of Linearization and Approximation01:29

Application of Linearization and Approximation

94
A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...
94
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

570
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...
570
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

277
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...
277
Application of the Linear Momentum Equation01:15

Application of the Linear Momentum Equation

429
The application of the linear momentum equation can be used to analyze the forces needed to hold a 180-degree pipe bend in place with flowing water. In this case, water flows through the bend with a constant cross-sectional area of 0.01 square meters and a flow velocity of 15 meters per second. The pressure at the entrance is 0.2 Megapascals and the pressure at the exit is 0.16 Megapascals.
The goal is to determine the force components in the x and y directions to hold the pipe in place. Since...
429
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

249
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...
249

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Hexahydrocannabinol-induced rhabdomyolysis and acute kidney injury: a case report combining comprehensive toxicokinetic and metabolomic investigations.

Journal of cannabis research·2026
Same author

Learning inherent genetic patterns and trait associations with deep generative models for discrete genotype simulation.

GigaScience·2026
Same author

Sante publique (Vandoeuvre-les-Nancy, France)·2026
Same author

Unveiling the Hidden Feast: A Model to Translate Molecular Detection Into Predation Rate-Application Example on Biological Control by Generalist Predators in Agricultural Fields.

Molecular ecology resources·2025
Same author

Variable Selection in High-Dimensional Logistic Regression Models Using a Whitening Approach.

IEEE transactions on computational biology and bioinformatics·2025
Same author

Easily Computed Marginal Likelihoods from Posterior Simulation Using the THAMES Estimator.

Bayesian analysis·2025

Related Experiment Video

Updated: Feb 5, 2026

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.4K

A variable selection approach in the multivariate linear model: an application to LC-MS metabolomics data.

Marie Perrot-Dockès1, Céline Lévy-Leduc1, Julien Chiquet1

  • 1UMR MIA-Paris, AgroParisTech, INRA - Université Paris-Saclay, 75005 Paris, France.

Statistical Applications in Genetics and Molecular Biology
|September 13, 2018
PubMed
Summary

This study introduces a new Lasso-based method for selecting variables in omic data, accounting for complex dependencies. The approach significantly enhances variable selection accuracy, particularly for time-series-like data.

Keywords:
metabolomicsmultivariate linear modeltime seriesvariable selection

More Related Videos

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

3.3K
Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data
04:57

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data

Published on: May 16, 2022

17.5K

Related Experiment Videos

Last Updated: Feb 5, 2026

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.4K
Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

3.3K
Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data
04:57

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data

Published on: May 16, 2022

17.5K

Area of Science:

  • Statistics
  • Bioinformatics
  • Genomics

Background:

  • Omic data often exhibit strong dependence structures due to biological processes or data acquisition methods.
  • Standard statistical methods may fail to account for these dependencies, leading to reduced power and spurious variable selection.

Purpose of the Study:

  • To propose a variable selection procedure within the multivariate linear model that explicitly addresses dependence between multiple responses.
  • To develop a novel Lasso-based approach that incorporates time-series-like covariance structures for the random error matrix.

Main Methods:

  • A multivariate linear model framework was employed.
  • A Lasso-based approach was developed, incorporating stationary process covariance structures for the random error matrix.
  • The method was validated through numerical experiments and applied to untargeted LC-MS data.

Main Results:

  • Estimating the covariance matrix of the random error matrix within the Lasso criterion significantly improved variable selection performance.
  • The proposed methodology demonstrated effectiveness in handling dependent omic data.

Conclusions:

  • The novel Lasso-based approach effectively accounts for response dependencies in omic data.
  • This method offers improved variable selection accuracy and is implemented in the R package MultiVarSel.