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

Gene-Environment Interactions01:20

Gene-Environment Interactions

758
Gene expression is a dynamic process that is significantly influenced by environmental factors. This interaction underlies the complex nature of biological development and the phenotypic differences observed among individuals, even among those with identical genetic makeups. Factors such as radiation, temperature, behavior, nutrition, and stress play pivotal roles in determining how genes are expressed. The concept of the reaction range is central to understanding this interaction. It posits...
758
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

117
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...
117
Measurement of Bioavailability: Pharmacodynamic Methods01:20

Measurement of Bioavailability: Pharmacodynamic Methods

39
Pharmacodynamic methods provide insights into a drug's effects on physiological processes over time and play a crucial role in understanding bioavailability and therapeutic efficacy. These methods can be broadly classified into acute pharmacological and therapeutic response approaches, each with distinct mechanisms and applications.The acute pharmacological response method directly correlates a drug's physiological effects, such as ECG or pupil diameter changes, to its time course in the body.
39
Dimensions of Health and Illness01:21

Dimensions of Health and Illness

9.1K
The factors influencing the health-illness continuum can be internal or external and may or may not be under conscious control. They are related to the following eight human dimensions, and each dimension is interrelated to one other.
9.1K
Multiple Regression01:25

Multiple Regression

3.3K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.3K
Modern Molecular Taxonomy01:29

Modern Molecular Taxonomy

317
Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...
317

You might also read

Related Articles

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

Sort by
Same author

Doubly Robust Estimators of the Restricted Mean Time in Favor Estimands in Individual- and Cluster-Randomized Trials.

Statistics in medicine·2026
Same author

Robust Heterogeneity Adjustment for Gaussian Graphical Model With Latent Variables.

Statistics in medicine·2026
Same author

The Optimal Effective Concentration of Spinal Anesthesia for Interlaminar Endoscopic Lumbar Discectomy: An Approach Based on the Biased Coin Design.

Drug design, development and therapy·2026
Same author

Dissolvable Microneedle Delivery of a Replication-Deficient Orthopoxvirus Vaccine: Formulation Screening and Immunogenicity Evaluation for Monkeypox Prevention.

Vaccines·2026
Same author

DNN-based semiparametric AFT model for integrating genomic and pathological imaging data in cancer prognosis.

Biometrics·2026
Same author

Integrating Omics and Pathological Imaging Data for Cancer Prognosis via a Deep Neural Network-Based Cox Model.

Statistics in medicine·2026

Related Experiment Video

Updated: Oct 30, 2025

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.7K

Multidimensional molecular measurements-environment interaction analysis for disease outcomes.

Yaqing Xu1, Mengyun Wu2, Shuangge Ma1

  • 1Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA.

Biometrics
|July 2, 2021
PubMed
Summary

This study introduces a new M-E interaction analysis method to examine how multiple molecular measurements and environmental risk factors influence complex diseases. The approach improves disease prediction by considering diverse biological data simultaneously.

Keywords:
environmental risk factorsinteraction analysismultidimensional molecular data

More Related Videos

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer
08:20

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer

Published on: May 21, 2019

5.8K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.9K

Related Experiment Videos

Last Updated: Oct 30, 2025

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.7K
Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer
08:20

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer

Published on: May 21, 2019

5.8K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.9K

Area of Science:

  • Genomics
  • Environmental Health
  • Computational Biology

Background:

  • Complex diseases arise from interactions between molecular factors (genetic, genomic, epigenetic) and environmental risks.
  • Previous studies analyzed only one molecular data type interacting with environmental factors.
  • Multidimensional profiling, collecting multiple molecular data types from individuals, is increasingly used in biomedical research.

Purpose of the Study:

  • To develop and validate a novel M-E (multidimensional molecular measurements and environmental risk factors) interaction analysis framework.
  • To effectively integrate and analyze overlapping and independent information from multiple molecular measurements and environmental factors.
  • To improve disease outcome prediction by comprehensively assessing molecular and environmental interactions.

Main Methods:

  • Developed an M-E interaction analysis method to accommodate multiple molecular data types (M) and environmental risk factors (E).
  • Utilized extensive simulations to compare the performance of the new method against existing approaches.
  • Applied the method to analyze The Cancer Genome Atlas (TCGA) data for lung adenocarcinoma and cutaneous melanoma.

Main Results:

  • The M-E interaction analysis method demonstrated superior performance compared to several related alternatives in simulations.
  • The analysis of TCGA data yielded stable and biologically significant findings for lung adenocarcinoma and cutaneous melanoma.
  • The study achieved reliable prediction of disease outcomes using the integrated multidimensional data.

Conclusions:

  • The proposed M-E interaction analysis is a powerful tool for understanding complex diseases by integrating diverse molecular and environmental data.
  • Simultaneous analysis of multiple molecular measurements alongside environmental factors enhances biological insights and predictive accuracy.
  • This approach offers a more comprehensive understanding of disease etiology and progression, paving the way for improved diagnostics and therapeutics.