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

Statistical Analysis: Overview01:11

Statistical Analysis: Overview

When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
Biostatistics: Overview01:20

Biostatistics: Overview

Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
Probability in Statistics01:14

Probability in Statistics

Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...

You might also read

Related Articles

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

Sort by
Same author

Unseen and underserved: the first multifactorial assessment of mental health among Paraguayan male prisoners.

Journal of mental health (Abingdon, England)·2026
Same author

Substance use patterns in elite athletes: a scoping review of alcohol, performance-enhancing drugs and other psychoactive substances.

British journal of sports medicine·2026
Same author

The "Mad Mothers of the Plaza de Mayo" and the pathologisation of sociopolitical dissent.

Australasian psychiatry : bulletin of Royal Australian and New Zealand College of Psychiatrists·2026
Same author

A cross-sectional study exploring mental health literacy among non-athlete personnel in elite-level road cycling.

Discover mental health·2026
Same author

"Every Woman Has a Different Cycle and Feels Differently": A Qualitative Study of Athlete-Centred Perspectives on Menstrual Cycle Symptoms and Management in Female Endurance Sports.

Sports (Basel, Switzerland)·2026
Same author

MYC serine 62 phosphorylation promotes its association with DNA double-strand breaks to facilitate repair and cell survival under genotoxic stress.

Genes & development·2026
Same journal

3DICE: Interpretable 3D Cross-Modal Learning for Drug-Target Interaction Prediction and Large-Scale Drug Discovery.

Bioinformatics (Oxford, England)·2026
Same journal

KASSPer: Kinase Active Site Structure Prediction using Protein and Ligand Language Models and Its Application to Virtual Screening.

Bioinformatics (Oxford, England)·2026
Same journal

IDR searcher: a search engine solution for public image resources.

Bioinformatics (Oxford, England)·2026
Same journal

KCFtools: Rapid alignment-free method for introgression screening and GWAS using k-mer profiles.

Bioinformatics (Oxford, England)·2026
Same journal

Meta2DB: Curated shotgun metagenomic feature sets and metadata for health state prediction.

Bioinformatics (Oxford, England)·2026
Same journal

conMItion: an R package adjusting confounding factors for associations in multi-omics.

Bioinformatics (Oxford, England)·2026
See all related articles

Related Experiment Video

Updated: Jun 27, 2026

Design, Surface Treatment, Cellular Plating, and Culturing of Modular Neuronal Networks Composed of Functionally Inter-connected Circuits
10:32

Design, Surface Treatment, Cellular Plating, and Culturing of Modular Neuronal Networks Composed of Functionally Inter-connected Circuits

Published on: April 15, 2015

SIMoNe: Statistical Inference for MOdular NEtworks.

Julien Chiquet1, Alexander Smith, Gilles Grasseau

  • 1UMR CNRS 8071 Statistique et Génome, 523, place des Terrasses, F-91000 Evry, France. julien.chiquet@genopole.cnrs.fr

Bioinformatics (Oxford, England)
|December 17, 2008
PubMed
Summary
This summary is machine-generated.

The SIMoNe R package infers gene regulatory networks using Gaussian graphical models and partial correlations from microarray data. It uniquely identifies latent network modules to improve statistical inference in complex biological systems.

More Related Videos

Two-Photon Polymerization 3D-Printing of Micro-scale Neuronal Cell Culture Devices
07:38

Two-Photon Polymerization 3D-Printing of Micro-scale Neuronal Cell Culture Devices

Published on: June 7, 2024

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
09:32

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Published on: December 18, 2016

Related Experiment Videos

Last Updated: Jun 27, 2026

Design, Surface Treatment, Cellular Plating, and Culturing of Modular Neuronal Networks Composed of Functionally Inter-connected Circuits
10:32

Design, Surface Treatment, Cellular Plating, and Culturing of Modular Neuronal Networks Composed of Functionally Inter-connected Circuits

Published on: April 15, 2015

Two-Photon Polymerization 3D-Printing of Micro-scale Neuronal Cell Culture Devices
07:38

Two-Photon Polymerization 3D-Printing of Micro-scale Neuronal Cell Culture Devices

Published on: June 7, 2024

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
09:32

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Published on: December 18, 2016

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Gene regulatory network inference is crucial for understanding cellular mechanisms.
  • Microarray data analysis presents challenges in high-dimensional and sparse settings.
  • Existing methods may not fully capture the modular nature of biological networks.

Purpose of the Study:

  • To introduce the SIMoNe R package for statistical inference of gene regulatory networks.
  • To model gene expression data using Gaussian graphical models (GGMs).
  • To leverage latent modular structures for enhanced network inference.

Main Methods:

  • Utilizes partial correlation coefficients to estimate gene-gene interactions.
  • Employs Gaussian graphical models to represent gene expression data.
  • Implements an adaptive penalization strategy guided by a latent modular structure.

Main Results:

  • The SIMoNe package facilitates the estimation of sparse concentration matrices.
  • It effectively identifies non-zero entries in the concentration matrix, representing network connections.
  • The approach allows for inference in high-dimensional gene expression datasets.

Conclusions:

  • SIMoNe provides a novel approach to gene regulatory network inference by incorporating modularity.
  • The R package is available under the GNU General Public License.
  • This method enhances the statistical inference of complex biological networks from microarray data.