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

Regression Toward the Mean01:52

Regression Toward the Mean

7.0K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
7.0K
Protein Networks02:26

Protein Networks

4.5K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.5K
Multiple Regression01:25

Multiple Regression

4.0K
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...
4.0K
Network Covalent Solids02:18

Network Covalent Solids

16.2K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.2K
Correlation and Regression00:53

Correlation and Regression

3.4K
In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
3.4K
Regression Analysis01:11

Regression Analysis

8.4K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
8.4K

You might also read

Related Articles

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

Sort by
Same author

Fokker-Planck Soft Actor-Critic.

IEEE transactions on neural networks and learning systems·2026
Same author

Mechanistic Atomic Hydrogen Chemistry for Ruthenium Deposition: From Ligand Elimination to Area-Selective Patterning.

Journal of the American Chemical Society·2026
Same author

Overcoming the Critical Thickness Limit: Interfacial Control of Crystallization Pathways in Atomic-Scale Dielectric Thin Films.

ACS nano·2026
Same author

Molecular dynamics simulation of nitrogen diffusion in iron and iron nitrides using <i>ab initio</i> data trained machine learning potentials.

Physical chemistry chemical physics : PCCP·2026
Same author

Tuning surface reactivity pathways through molecular inhibitor redosing for precision nanopatterning.

Materials horizons·2026
Same author

A self-regulating wearable OLED patch for accelerated wound healing <i>via</i> photobiomodulation-triggered drug delivery.

Materials horizons·2026
Same journal

GMSA: A Graph Matching and Point Cloud Registration-Based Method for Spatial Transcriptomics Data Alignment.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

Investigations on Multiple Protein Scaffold Filling.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

Cell Type Prediction for Single-Cell RNA Sequencing Utilizing Unsupervised Domain Adaptation and Semi-Supervised Learning.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

PPIGAN: Prediction of Protein-Protein Interactions Using Generative Adversarial Networks.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

Deep Structure-Enhanced Cell Clustering Model for Single-Cell RNA Sequencing Data.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

Asymmetric Drug-Drug Interaction Prediction Based on Generative Adversarial Networks and Knowledge Graph.

Journal of computational biology : a journal of computational molecular cell biology·2026
See all related articles

Related Experiment Video

Updated: Jan 30, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.9K

Regression-Based Network Estimation for High-Dimensional Genetic Data.

Kyu Min Lee1, Minhyeok Lee2, Junhee Seok2

  • 11 School of Industrial Management Engineering, Korea University, Seoul, South Korea.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|January 18, 2019
PubMed
Summary
This summary is machine-generated.

New regression methods improve gene network estimation from high-dimensional gene expression data. These approaches, using elastic-net and adaptive elastic-net, effectively handle multicollinearity and hub nodes, outperforming conventional techniques.

Keywords:
adaptive elastic-netgene network estimationgraphical modelregression-based approach

More Related Videos

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
06:09

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

Published on: September 8, 2023

952
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

Related Experiment Videos

Last Updated: Jan 30, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.9K
P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
06:09

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

Published on: September 8, 2023

952
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

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Genome sequencing generates vast gene expression data, increasing dimensionality.
  • High-dimensional data poses challenges for gene network estimation, including multicollinearity and the presence of hub nodes.

Purpose of the Study:

  • To develop robust regression-based methods for gene network estimation.
  • To address challenges posed by high-dimensional data and hub nodes in genetic information.

Main Methods:

  • Proposed elastic-net and adaptive elastic-net penalty regressions.
  • Compared proposed methods with conventional regression approaches like LASSO and adaptive LASSO.
  • Applied methods to actual genetic data for network estimation.

Main Results:

  • Regression-based approaches demonstrated superior performance in simulations.
  • Proposed methods showed robustness when handling high-dimensional gene expression data.
  • Successful application to real genetic data confirmed suitability for gene network estimation.

Conclusions:

  • Elastic-net and adaptive elastic-net regressions are effective for gene network estimation.
  • The proposed methods provide a robust solution for analyzing complex, high-dimensional genetic data.
  • These advancements aid in understanding gene regulatory networks from large-scale expression datasets.