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

DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

You might also read

Related Articles

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

Sort by
Same author

Cross-dataset annotation harmonization for cell-type hierarchy construction.

Bioinformatics (Oxford, England)·2026
Same author

Benchmarking AI scientists for omics data-driven biological discovery.

Bioinformatics (Oxford, England)·2026
Same author

Identifying fate-determining transcription factors with single-cell omics.

Trends in genetics : TIG·2026
Same author

A multi-modal diffusion model with dual-cross-attention for multi-omics data generation and translation.

Nature communications·2026
Same author

Minimizing far-extending chromatin perturbation in genome editing preserves stem cell identity.

Cell stem cell·2026
Same author

Current opinions on large cellular models.

Quantitative biology (Beijing, China)·2026

Related Experiment Video

Updated: Jul 7, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Finding distinct biclusters from background in gene expression matrices.

Zhengpeng Wu1, Jiangni Ao, Xuegong Zhang

  • 1Bioinformatics Division, TNLIST and Department of Automation, Tsinghua University, Beijing 100084, PR China.

Bioinformation
|February 29, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces a new biclustering method, Distinct 2-Dimensional Pattern Finder (D2D), for analyzing microarray data. D2D effectively finds gene and sample subsets without predefined patterns, showing robustness against noise and parameter variations.

Keywords:
Distinct 2-Dimensional (D2D)biclustersgene expression matricesnoisesimulation

More Related Videos

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Related Experiment Videos

Last Updated: Jul 7, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Biclustering is crucial for analyzing high-dimensional microarray data.
  • Existing methods often rely on predefined bicluster patterns, limiting their flexibility.
  • Identifying homogeneous subsets of genes and samples distinct from background noise is challenging.

Purpose of the Study:

  • To develop a novel biclustering method that identifies significant 2D patterns without requiring predefined patterns.
  • To introduce the Distinct 2-Dimensional Pattern Finder (D2D) algorithm.
  • To evaluate the performance and robustness of the D2D method.

Main Methods:

  • The Distinct 2-Dimensional Pattern Finder (D2D) method employs iterative matrix reordering using a novel similarity measure.
  • A flexible scanning-and-growing step is utilized to identify biclusters.
  • The method was tested on diverse simulation datasets with varying noise levels and cluster overlaps.

Main Results:

  • D2D consistently performed well across various simulation conditions, outperforming existing methods.
  • The D2D method demonstrated robustness against noise, overlapping clusters, and parameter settings.
  • Experiments confirmed D2D's ability to efficiently discover diverse bicluster types with distinctive features.

Conclusions:

  • The D2D method offers a flexible and robust approach to biclustering for microarray data analysis.
  • D2D overcomes limitations of pattern-dependent methods by identifying biclusters based on distinct features.
  • The developed algorithm is capable of discovering various bicluster types, enhancing biological data interpretation.