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,...

Protein Networks

Protein Networks

You might also read

Related Articles

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

Sort by

Same author

Mapping the future of medicine through digital twins.

Frontiers in molecular medicine·2026

Same author

Implementing physics-informed neural networks with deep learning for differential equations.

Frontiers in artificial intelligence·2026

Same author

Screening for Celiac Disease in Childhood: Cost-Effectiveness of Multiple Genetic and Serological Testing Approaches.

Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association·2025

Same author

The role of digital twins in P4 medicine: A paradigm for modern healthcare.

NPJ digital medicine·2025

Same author

Frigate: A Novel Dataset Comprising Wide Field-of-View Astronomical FITS Images of Low Earth Orbit Scenes for Machine Learning Applications.

Scientific data·2025

Same author

A mixture of attention experts-embedded flow-based generative model to create synthetic cells in single-cell RNA-Seq datasets.

PLoS computational biology·2025

Same journal

Proteins Rich in Prime Mass Residues and Their Role in Cell Biology.

Evolutionary bioinformatics online·2026

Same journal

Hybrid Feature Selection-Based Machine Learning and Deep Learning Framework for Biomarker Prediction From RNA-seq Data During Dengue Fever to Severe Dengue Progression.

Evolutionary bioinformatics online·2026

Same journal

Functional Annotation and Molecular Characterization of a Hypothetical Protein From <i>Mycobacterium leprae</i> Through <i>In Silico</i> Approaches: A Potential Therapeutic Target.

Evolutionary bioinformatics online·2026

Same journal

Evolutionary Bioinformatics Expands its Breadth.

Evolutionary bioinformatics online·2026

Same journal

Gene Expression Underlying Teloblast Formation in Leech (Annelida: Hirudinae).

Evolutionary bioinformatics online·2026

Same journal

Computational Optimization of CRISPR-Cas13a sgRNAs Targeting the SARS-CoV-2 Spike Gene for SHERLOCK-Based Diagnostics.

Evolutionary bioinformatics online·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 3, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Netmes: assessing gene network inference algorithms by network-based measures.

Gökmen Altay¹, Zeyneb Kurt², Matthias Dehmer³

¹Biomedical Engineering, Bahçeşehir University, Beşiktaş, Istanbul, Turkey.

Evolutionary Bioinformatics Online

|February 15, 2014

Summary

This summary is machine-generated.

We present netmes, an R package for assessing gene regulatory network inference (GRNI) algorithms. It facilitates the use of network-based error measures and ensemble approaches for evaluating GRNI performance.

Keywords:

R package for the network-based measures gene regulatory networks global network-based measures local network-based measures metrics for assessing ensemble datasets

More Related Videos

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

Related Experiment Videos

Last Updated: May 3, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

Area of Science:

Bioinformatics
Computational Biology
Systems Biology

Background:

Gene regulatory network inference (GRNI) algorithms are crucial for analyzing large-scale microarray data to understand cellular molecular interactions.
Assessing GRNI performance commonly involves network-based error metrics combined with ensemble methods.
There is a need for accessible tools to implement these assessment strategies.

Purpose of the Study:

To introduce netmes, a novel R software package designed for the assessment of GRNI algorithms.
To provide a user-friendly tool for applying network-based error measures and ensemble approaches in GRNI evaluation.

Main Methods:

Development of the netmes R package.
Implementation of various network-based error measures.
Integration of ensemble approaches for GRNI performance assessment.

Main Results:

The netmes package offers a streamlined approach to evaluating GRNI algorithms.
It enables researchers to apply sophisticated metrics for assessing inference accuracy.
The package simplifies the process of comparing different GRNI methods.

Conclusions:

netmes enhances the evaluation of gene regulatory network inference algorithms.
The R package promotes more robust and reliable assessment of GRNI methods.
netmes is available for the scientific community via R-Forge.