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

Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

7.1K
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...
7.1K
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

8.2K
The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
8.2K
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

3.7K
No description available
3.7K
Protein Networks02:26

Protein Networks

2.9K
No description available
2.9K
Protein Networks02:26

Protein Networks

4.6K
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,...
4.6K
Synteny and Evolution02:31

Synteny and Evolution

3.8K
John H. Renwick first coined the term “synteny” in 1971, which refers to the genes present on the same chromosomes, even if they are not genetically linked. The species with common ancestry tend to show conserved syntenic regions. Therefore, the concept of synteny is nowadays used to describe the evolutionary relationship between species.
Around 80 million years ago, the human and mice lineages diverged from the common ancestor. During the course of evolution, the ancestral...
3.8K

You might also read

Related Articles

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

Sort by
Same author

KEGG Syntax for comparison of organisms, organism groups, and viruses by conserved gene repertoires.

Protein science : a publication of the Protein Society·2026
Same author

Acceleration of FM-index Queries Through Prefix-free Parsing.

Algorithms in bioinformatics : ... International Workshop, WABI ..., proceedings. WABI (Workshop)·2025
Same author

KEGG: biological systems database as a model of the real world.

Nucleic acids research·2024
Same author

Pfp-fm: an accelerated FM-index.

Algorithms for molecular biology : AMB·2024
Same author

PFP-FM: An Accelerated FM-index.

Research square·2023
Same author

KEGG tools for classification and analysis of viral proteins.

Protein science : a publication of the Protein Society·2023
Same journal

CNV-ECOD: A copy number variation detection method based on ECOD algorithm using next-generation sequencing data.

Journal of bioinformatics and computational biology·2026
Same journal

ReinVar: A model-free paradigm-based reinforcement learning approach to detect copy number variation.

Journal of bioinformatics and computational biology·2026
Same journal

When pipelines run but coordinates fail: A simple spatial specificity check for false locality in post-GWAS analysis.

Journal of bioinformatics and computational biology·2026
Same journal

Comparative benchmarking of template-based, evolutionary-diffusion, and generative language models for IsPETase structure prediction.

Journal of bioinformatics and computational biology·2026
Same journal

Trap spaces as labelled ideals of SCC posets: A structural-functional theory of reachability in asynchronous boolean networks.

Journal of bioinformatics and computational biology·2026
Same journal

Erratum - DDINet: Drug-drug interaction prediction network based on multi-molecular fingerprint features and multi-head attention centered weighted autoencoder.

Journal of bioinformatics and computational biology·2026
See all related articles

Related Experiment Video

Updated: Feb 17, 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

2.7K

Utilizing evolutionary information and gene expression data for estimating gene networks with bayesian network

Yoshinori Tamada1, Hideo Bannai, Seiya Imoto

  • 1Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto, 611-0011, Japan. tamada@kuicr.kyoto-u.ac.jp

Journal of Bioinformatics and Computational Biology
|December 24, 2005
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical method to build gene networks using gene expression data and evolutionary information. The approach improves accuracy by leveraging conserved gene functions across species, aiding in the discovery of novel gene relationships.

More Related Videos

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq
07:09

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

10.5K
Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
08:57

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

16.6K

Related Experiment Videos

Last Updated: Feb 17, 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

2.7K
A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq
07:09

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

10.5K
Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
08:57

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

16.6K

Area of Science:

  • Systems Biology
  • Bioinformatics
  • Computational Biology

Background:

  • Gene expression data alone is insufficient for accurate gene network estimation.
  • Conserved proteins across species often share similar functions and regulatory roles.
  • Evolutionary information can refine gene regulatory network inference.

Purpose of the Study:

  • To develop a statistical method for improved gene network estimation.
  • To integrate evolutionary information into gene network modeling.
  • To simultaneously infer gene networks from multiple organisms' expression data.

Main Methods:

  • A Bayesian network model was employed for gene network estimation.
  • The method utilizes evolutionarily conserved relationships between genes.
  • Simultaneous estimation of gene networks from two distinct organisms' data was performed.

Main Results:

  • The proposed method effectively estimates gene networks by integrating cross-species evolutionary data.
  • Analysis on Saccharomyces cerevisiae and Homo sapiens cell cycle data demonstrated method effectiveness.
  • The approach successfully identified known and potentially novel gene relationships.

Conclusions:

  • Integrating evolutionary information significantly enhances gene network inference accuracy.
  • The developed statistical method provides a robust framework for cross-species gene network analysis.
  • This approach facilitates the discovery of conserved and novel gene regulatory mechanisms.