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

Protein Networks

Protein Networks

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,...

You might also read

Related Articles

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

Sort by

Same author

tvsfglasso: Time-varying scale-free graphical lasso for network estimation from time-series data.

PLoS computational biology·2025

Same author

HMFGraph: Novel Bayesian approach for recovering biological networks.

PLoS computational biology·2025

Same author

Robust multi-outcome regression with correlated covariate blocks using fused LAD-lasso.

Journal of applied statistics·2025

Same author

Posterior estimation of longitudinal variance components from nonlongitudinal data using Bayesian Gaussian process model.

Genetics·2025

Same author

Timing based clustering of childhood BMI trajectories reveals differential maturational patterns; Study in the Northern Finland Birth Cohorts 1966 and 1986.

International journal of obesity (2005)·2025

Same author

Principal component analysis revisited: fast multitrait genetic evaluations with smooth convergence.

G3 (Bethesda, Md.)·2024

Same journal

Adaptive Dynamics of Quantitative Traits in a Steadily Changing Environment.

Genetics·2026

Same journal

Functional Landscape of Zebrafish Gonadotropins and Receptors: A Comprehensive Genetic Analysis.

Genetics·2026

Same journal

Synergistic actions of Nup43 and Myosin VI drive actin cone assembly during Drosophila spermiogenesis.

Genetics·2026

Same journal

Identification of two Cryptococcus neoformans heme transporters involved in Fhb1-mediated nitrosative stress protection in a fission yeast model.

Genetics·2026

Same journal

Analysis of a hypomorphic mei-P26 mutation reveals coordination between developmental programming of germ cells and meiotic chromosome dynamics.

Genetics·2026

Same journal

Neural and Genetic Mechanisms Regulating Copulation Latency in Male Drosophila melanogaster.

Genetics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 7, 2025

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer

Published on: November 2, 2013

Network hub gene detection using the entire solution path information.

Markku Kuismin¹, Mikko J Sillanpää¹

¹Research Unit of Mathematical Sciences, University of Oulu, P.O. BOX 8000, Oulu FI-90014, Finland.

|November 13, 2024

Summary

This summary is machine-generated.

This study introduces a new method for identifying crucial hub genes in gene co-expression networks. The Mean Degree Squared Distance (MDSD) statistic effectively balances identifying true positive and minimizing false positive hub genes.

Keywords:

gene co-expression networks high-dimensional data hub glasso hub identification network learning penalization methods

More Related Videos

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

Published on: May 22, 2018

Fluorescence-microscopy Screening and Next-generation Sequencing: Useful Tools for the Identification of Genes Involved in Organelle Integrity

Fluorescence-microscopy Screening and Next-generation Sequencing: Useful Tools for the Identification of Genes Involved in Organelle Integrity

Published on: April 13, 2012

Related Experiment Videos

Last Updated: Jun 7, 2025

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer

Published on: November 2, 2013

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

Published on: May 22, 2018

Fluorescence-microscopy Screening and Next-generation Sequencing: Useful Tools for the Identification of Genes Involved in Organelle Integrity

Fluorescence-microscopy Screening and Next-generation Sequencing: Useful Tools for the Identification of Genes Involved in Organelle Integrity

Published on: April 13, 2012

Area of Science:

Computational Biology
Bioinformatics
Systems Biology

Background:

Gene co-expression networks are vital for understanding gene regulation.
Hub genes play critical roles in network organization and function.
Existing methods for hub gene identification often rely on single optimal models.

Purpose of the Study:

To develop a novel method for robust hub gene detection in gene co-expression networks.
To leverage information across multiple graphical models along the solution path.
To introduce the Mean Degree Squared Distance (MDSD) statistic for hub gene identification.

Main Methods:

Utilizing data-driven graphical models for gene network estimation.
Aggregating information from multiple models along the regularization parameter solution path.
Introducing the Mean Degree Squared Distance (MDSD) statistic, analogous to Cook's distance, to quantify node influence.

Main Results:

The MDSD statistic effectively identifies hub genes by amalgamating node information.
Simulation and empirical studies confirm MDSD's ability to balance false positive and true positive rates.
The proposed method offers an alternative to traditional single-model approaches.

Conclusions:

The MDSD statistic provides a stable and reliable approach for hub gene detection.
This method enhances the identification of key regulatory genes in complex biological networks.
An R package 'MDSD' is available for public use, facilitating broader application.