Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Genomics

Genomics

Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...

Multi-input and Multi-variable systems

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

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...

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for k_a 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...

Multiple Allele Traits

Multiple Allele Traits

The Concept of Multiple Allelism

Multiple Regression

Multiple Regression

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...

You might also read

Related Articles

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

Sort by

Same author

Subgroup identification via Interaction Tree and Mixed Model for Repeated Measures with application to Alzheimer's disease.

Biometrics·2026

Same author

Cox-MK: a model-X knockoff framework for genome-wide survival association analysis.

Genetics·2026

Same author

Unsupervised Sparse Multi-Task Learning With Application to Alzheimer's Disease.

Statistics in medicine·2026

Same author

Deep Neural Network With a Smooth Monotonic Output Layer for Dynamic Risk Prediction.

Statistics in medicine·2026

Same author

Cross-ancestry information transfer framework improves protein abundance prediction and protein-trait association identification.

Briefings in bioinformatics·2026

Same author

Reducing Inter-Individual Differences in Task fMRI Preprocessing with OGRE (One-Step General Registration and Extraction) Preprocessing.

Neuroinformatics·2025

Search research articles

Related Experiment Video

Updated: Nov 16, 2025

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

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

Published on: September 20, 2024

High-dimensional integrative copula discriminant analysis for multiomics data.

Yong He¹, Hao Chen², Hao Sun²

¹Shandong University, Jinan, China.

Statistics in Medicine

|February 22, 2021

Summary

This summary is machine-generated.

This study introduces a new integrative classifier using multiomics data for better disease prediction. The method combines gene expression and DNA methylation data, improving upon traditional approaches.

Keywords:

Gaussian copula data mining discriminant analysis integrative analysis machine learning

More Related Videos

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Related Experiment Videos

Last Updated: Nov 16, 2025

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

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

Published on: September 20, 2024

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

Biomedical data science
Statistical genetics
Bioinformatics

Background:

Multiomics data integration is crucial for advancing human health and disease understanding.
Traditional classification methods often rely on restrictive assumptions, limiting their power with complex biological data.

Purpose of the Study:

To propose a novel integrative copula discrimination analysis classifier for two-class classification problems.
To leverage information from multiple omics data types within a discriminant analysis framework.
To relax the common Gaussian assumption in classification models.

Main Methods:

Developed an integrative copula discrimination analysis classifier.
Employed a two-class classification approach.
Validated the method using numerical simulations for finite sample performance assessment.

Main Results:

The proposed classifier demonstrates effectiveness in integrating diverse omics data.
Numerical studies confirm the classifier's robust finite sample performance.
Application to the ROSMAP study shows improved prediction by integrating gene expression and DNA methylation data.

Conclusions:

The integrative copula discrimination analysis classifier offers a powerful new tool for biomedical research.
Integrating multiple omics data types, such as gene expression and DNA methylation, enhances predictive accuracy.
This approach holds promise for a deeper understanding of complex diseases.