Jove
Visualize
Contact Us

Related Concept Videos

Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a survival tree begins...
Phylogenetic Trees03:21

Phylogenetic Trees

Phylogenetic trees come in many forms. It matters in which sequence the organisms are arranged from the bottom to the top of the tree, but the branches can rotate at their nodes without altering the information. The lines connecting individual nodes can be straight, angled, or even curved.The length of the branches can depict time or the relative amount of change among organisms. For instance, the branch length might indicate the number of amino acid changes in the sequence that underlies the...
Phylogenetic Trees03:21

Phylogenetic Trees

Phylogenetic trees come in many forms. It matters in which sequence the organisms are arranged from the bottom to the top of the tree, but the branches can rotate at their nodes without altering the information. The lines connecting individual nodes can be straight, angled, or even curved.The length of the branches can depict time or the relative amount of change among organisms. For instance, the branch length might indicate the number of amino acid changes in the sequence that underlies the...
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
Microbial Phylogeny01:28

Microbial Phylogeny

Understanding the evolutionary relationships among microorganisms is fundamental to microbial ecology and taxonomy. Phylogenetic trees are essential tools for inferring these relationships, relying primarily on comparative analyses of molecular sequences such as DNA, RNA, or proteins. In microbial studies, these trees typically depict the evolutionary paths of diverse bacterial and archaeal species by mapping genetic differences accumulated over time.Phylogenetic trees are composed of tips,...
Regression Analysis01:11

Regression Analysis

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:

You might also read

Related Articles

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

Sort by
Same author

Improving classification on imbalanced genomic data via KDE-based synthetic sampling.

BioData mining·2025
Same author

Classification performance assessment for imbalanced multiclass data.

Scientific reports·2024
Same author

Advance Monitoring of COVID-19 Incidence Based on Taxi Mobility: The Infection Ratio Measure.

Healthcare (Basel, Switzerland)·2024
Same author

Addressing energy challenges in Iraq: Forecasting power supply and demand using artificial intelligence models.

Heliyon·2024
Same author

SigPrimedNet: A Signaling-Informed Neural Network for scRNA-seq Annotation of Known and Unknown Cell Types.

Biology·2023
Same author

Ten quick tips for biomarker discovery and validation analyses using machine learning.

PLoS computational biology·2022
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 Experiment Video

Updated: Jun 8, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

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

Published on: December 7, 2021

Inferring gene regression networks with model trees.

Isabel A Nepomuceno-Chamorro1, Jesus S Aguilar-Ruiz, Jose C Riquelme

  • 1Dpt Lenguajes y Sistemas Informaticos, Universidad de Sevilla, Seville, Spain. inepomuceno@us.es

BMC Bioinformatics
|October 19, 2010
PubMed
Summary

We introduce REGNET, a novel method using model trees to build gene regulatory networks from gene expression data. REGNET accurately identifies gene interactions, outperforming traditional correlation-based approaches by detecting local similarities.

More Related Videos

A Practical Guide to Phylogenetics for Nonexperts
12:00

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
03:37

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

Related Experiment Videos

Last Updated: Jun 8, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

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

Published on: December 7, 2021

A Practical Guide to Phylogenetics for Nonexperts
12:00

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
03:37

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

Area of Science:

  • Bioinformatics
  • Systems Biology
  • Computational Biology

Background:

  • Microarray technologies generate vast amounts of data, necessitating advanced strategies for analysis.
  • Inferring gene regulatory networks is crucial for understanding biological systems, with gene co-expression networks as a key first step.
  • Current correlation-based methods for gene co-expression networks excel at detecting global similarities but often miss local gene interactions.

Purpose of the Study:

  • To develop a novel computational method for inferring gene interaction networks from gene expression data.
  • To address the limitations of correlation-based methods in detecting local gene similarities.
  • To provide a more accurate approach for constructing gene association networks.

Main Methods:

  • Propose REGNET, a method utilizing model trees to construct gene interaction networks.
  • For each gene, a single regression tree is generated using the remaining genes as predictors.
  • A statistical procedure to control the false discovery rate is applied to build the final gene association network graph.

Main Results:

  • REGNET was experimentally validated on Saccharomyces Cerevisiae and E.coli datasets.
  • The biological coherence of REGNET's inferred networks was assessed.
  • REGNET demonstrated superior accuracy in detecting true gene associations compared to Pearson and Spearman correlation-based methods when validated against the E.coli transcriptional network.

Conclusions:

  • REGNET effectively generates gene association networks from gene expression data, calculating relationships simultaneously.
  • Model trees offer precise estimation of target gene values via linear regression functions, excelling at localized similarities.
  • Experimental results confirm the robust performance and accuracy of the REGNET approach.