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

Frequency-dependent Selection01:21

Frequency-dependent Selection

24.4K
When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
24.4K
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

434
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
434
Longitudinal Studies01:26

Longitudinal Studies

614
Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
614
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

388
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
388
Survival Tree01:19

Survival Tree

462
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
462
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

302
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...
302

You might also read

Related Articles

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

Sort by
Same author

Air-permeable hydrogels through viscoelastic phase separation of aerogels.

Nature·2026
Same author

MTHFR, Pneumonia, and Thrombosis in Children: A Gene-Infection Interaction.

Pediatrics·2026
Same author

Electric fields trigger ceramide-dependent vesicle budding and boost the generation of small extracellular vesicles.

Communications biology·2026
Same author

A compact low-power magnetic particle imaging scanner based on a permanent-magnet field-free-line generator with high gradient.

The Review of scientific instruments·2026
Same author

Discussion on "INTACT: A method for integration of longitudinal physical activity data from multiple sources" by Jingru Zhang, Erjia Cui, Hongzhe Li, and Haochang Shou.

Biometrics·2026
Same author

Sinomenine restrains the proliferation and hyperactivation of B lymphocytes partly by inhibiting interferon regulatory factor 5.

Journal of ethnopharmacology·2026

Related Experiment Video

Updated: Mar 15, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

8.1K

FEATURE SCREENING FOR TIME-VARYING COEFFICIENT MODELS WITH ULTRAHIGH DIMENSIONAL LONGITUDINAL DATA.

Wanghuan Chu1, Runze Li2, Matthew Reimherr3

  • 1Department of Statistics, Pennsylvania State University, State College, PA, 16801, USA, wxc228@psu.edu.

The Annals of Applied Statistics
|September 16, 2016
PubMed
Summary
This summary is machine-generated.

A new screening method for varying coefficient models improves predictions for longitudinal data. This approach identified key genetic mutations linked to lung function in children with asthma.

Keywords:
Feature SelectionFunctional Linear ModelGenome-Wide Association StudyTime-varying Coefficient ModelsUltrahigh Dimensional Longitudinal Data

More Related Videos

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

1.4K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.2K

Related Experiment Videos

Last Updated: Mar 15, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

8.1K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

1.4K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.2K

Area of Science:

  • Biostatistics
  • Genetics
  • Epidemiology

Background:

  • Childhood asthma management is complex, influenced by genetic and longitudinal factors.
  • Existing statistical models may not adequately capture ultrahigh-dimensional longitudinal data in asthma research.
  • The Childhood Asthma Management Project (CAMP) provides a valuable dataset for methodological development.

Purpose of the Study:

  • To develop and validate a novel screening procedure for varying coefficient models with ultrahigh-dimensional longitudinal predictors.
  • To enhance the statistical analysis of complex datasets like those from CAMP.
  • To identify genetic factors influencing lung function in children with asthma.

Main Methods:

  • Introduction of a new screening procedure for varying coefficient models.
  • Utilizing ultrahigh-dimensional longitudinal predictor variables.
  • Performance evaluation through Monte Carlo simulations and numerical comparisons.

Main Results:

  • The proposed procedure significantly outperforms existing methods.
  • Substantial improvements were observed in explained variability and prediction error.
  • Identification of potentially important genetic mutations related to lung function.

Conclusions:

  • The new screening procedure offers a powerful tool for analyzing ultrahigh-dimensional longitudinal data.
  • The identified genetic mutations provide new insights into asthma pathogenesis and lung function.
  • Nonlinear patterns in genetic effects around puberty were observed, suggesting developmental influences.