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 Experiment Video

Updated: May 19, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Seed-based biclustering of gene expression data.

Jiyuan An1, Alan Wee-Chung Liew, Colleen C Nelson

  • 1Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia. j.an@qut.edu.au

Plos One
|August 11, 2012
PubMed
Summary

This study introduces a novel seed-based biclustering algorithm to efficiently identify coherent gene clusters within gene expression datasets. The method systematically finds gene sets with similar expression patterns across subsets of experimental conditions.

Related Concept Videos

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...

You might also read

Related Articles

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

Sort by
Same author

3D TractFormer: 3D Direct Volumetric White Matter Tract Segmentation with Hybrid Channel-Wise Transformer.

Sensors (Basel, Switzerland)·2026
Same author

Harnessing Bulk-Segregant Mapping to Identify Trait-Associated Genes in the Allopolyploid Model Plant Nicotiana benthamiana.

Plant biotechnology journal·2026
Same author

Bulk RNA sequencing dataset of Claudin-low breast cancer cell lines with Neuropilin-1 knockdown.

Scientific data·2025
Same author

A Hybrid Bidirectional Deep Learning Model Using HRV for Prediction of ICU Mortality Risk in TBI Patients.

Journal of healthcare informatics research·2025
Same author

Explainable multimodal hematology analysis for white blood cell classification and attribute prediction.

Computers in biology and medicine·2025
Same author

Identifying key physiological and clinical factors for traumatic brain injury patient management using network analysis and machine learning.

PloS one·2025

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Biological functions arise from complex gene networks, not individual genes.
  • Traditional gene clustering using microarray data struggles with condition-specific activity.
  • Biclustering identifies gene clusters with coherent expression across subsets of conditions.

Purpose of the Study:

  • To propose a novel seed-based algorithm for identifying additive biclusters.
  • To efficiently and exhaustively find coherent gene clusters in gene expression data.

Main Methods:

  • Exhaustively selects gene and condition combinations as seeds for candidate bicluster tables.
  • Identifies genes with dissimilar expression levels across a subset of conditions.
  • Systematically tests gene sets against a minimum threshold to identify biclusters.

Related Experiment Videos

Last Updated: May 19, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Main Results:

  • The algorithm exhaustively identifies additive biclusters.
  • The seed-based approach allows for efficient and systematic bicluster discovery.

Conclusions:

  • Presents a novel biclustering algorithm for identifying additive biclusters.
  • The exhaustive testing of gene-condition combinations facilitates bicluster identification.