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

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

253
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...
253
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

166
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...
166
Cluster Sampling Method01:20

Cluster Sampling Method

13.7K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
13.7K
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

353
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
353
Response Surface Methodology01:16

Response Surface Methodology

401
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
401
Multiple Regression01:25

Multiple Regression

3.4K
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...
3.4K

You might also read

Related Articles

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

Sort by
Same author

Predicting complex phenotypes using multi-omics data in maize.

The Plant cell·2026
Same author

CAPS-Based SNP Genotyping for Nitrogen-Response Phenotypes in Maize Hybrids.

Bio-protocol·2025
Same author

Fifteenth century CE Bolivian maize reveals genetic affinities with ancient Peruvian maize.

eLife·2025
Same author

Phenotypic plasticity in maize grain yield: Genetic and environmental insights of response to environmental gradients.

The plant genome·2025
Same author

Position: Topological Deep Learning is the New Frontier for Relational Learning.

Proceedings of machine learning research·2025
Same author

Genome-Wide Association Study of Cuticle and Lipid Droplet Properties of Cucumber (<i>Cucumis sativus</i> L.) Fruit.

International journal of molecular sciences·2024
Same journal

Inner layer security reinforcement for instant payment systems: a dual layer encryption-steganography evaluation in Brunei's digital payment context.

Frontiers in big data·2026
Same journal

Measuring the impact of virtualization and containerization on the environment when using GPUs for processing the AI models.

Frontiers in big data·2026
Same journal

Using artificial intelligence to improve governance and public services in Africa.

Frontiers in big data·2026
Same journal

Case count metric for comparative analysis of entity resolution results.

Frontiers in big data·2026
Same journal

Data field theory: a geometric framework for learning on Riemannian manifolds with synthetic validation and limitation analysis.

Frontiers in big data·2026
Same journal

Correction: Explainable gradient convolutional vector fuzzy pattern analysis based on ensemble model for facial expression recognition.

Frontiers in big data·2026
See all related articles

Related Experiment Video

Updated: Nov 14, 2025

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

7.8K

Simultaneous Parameter Learning and Bi-clustering for Multi-Response Models.

Ming Yu1, Karthikeyan Natesan Ramamurthy2, Addie Thompson3,4

  • 1Booth School of Business, The University of Chicago, Chicago, IL, United States.

Frontiers in Big Data
|March 11, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces new methods for uncovering hidden group structures in complex regression models, improving data analysis for applications like Genome-Wide Association Studies (GWAS). The approach enhances understanding of biological relationships.

Keywords:
bi-clusteringconvex clusteringgenome-wide association studieshigh-throughput phenotypingmultitask learningsparse linear regression

More Related Videos

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.7K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.2K

Related Experiment Videos

Last Updated: Nov 14, 2025

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

7.8K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.7K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.2K

Area of Science:

  • Statistics
  • Machine Learning
  • Bioinformatics

Background:

  • Multi-response and multi-task regression models are crucial for analyzing complex datasets.
  • Identifying underlying group structures in parameter matrices is challenging but essential for applications like Genome-Wide Association Studies (GWAS).
  • Existing methods may not fully capture intricate grouping patterns, such as bi-cluster or checkerboard structures.

Purpose of the Study:

  • To develop novel formulations for simultaneously inferring parameter matrices and their group structures in regression models.
  • To enhance the discovery of complex grouping patterns, including task-specific, feature-specific, and combined structures.
  • To provide a robust framework for applications requiring the elucidation of relationships between variables, such as in genetic studies.

Main Methods:

  • Proposing two novel formulations based on convex regularization penalties for joint parameter and structure learning.
  • Developing efficient optimization algorithms to solve the proposed convex problems.
  • Providing theoretical guarantees for the numerical convergence of the optimization approaches.

Main Results:

  • Demonstrating significantly improved clustering quality compared to existing state-of-the-art methods through extensive experiments.
  • Validating the effectiveness of the proposed approaches on real-world datasets, specifically analyzing plant variety phenotypes and genotypes.
  • Successfully inferring both parameter matrices and their associated group structures.

Conclusions:

  • The proposed convex regularization-based methods effectively identify unknown grouping structures in multi-response and multi-task regression.
  • These methods offer valuable insights into data mechanisms, particularly demonstrated in the context of Genome-Wide Association Studies (GWAS).
  • The validated performance on real biological data highlights the practical utility and robustness of the approach for complex biological data analysis.