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 Videos

Techniques for clustering gene expression data.

G Kerr1, H J Ruskin, M Crane

  • 1Biocomputation Research Lab (Modelling and Scientific Computing Group, School of Computing) and National Institute of Cellular Biotechnology, Dublin City University, Dublin 9, Ireland. gkerr@computing.dcu.ie

Computers in Biology and Medicine
|December 7, 2007
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

The current practice of volumetric modulated arc therapy for breast cancer in Europe - A survey by the EFOMP VMAT breast working group.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)·2025
Same author

Predicting the bodily self in space and time.

Scientific reports·2024
Same author

Features and frequency of use of electronic health records in primary care across 20 countries: a cross-sectional study.

Public health·2024
Same author

Association Between Clinical Frailty Scale Score and Length of Stay in a Complex Discharge Unit.

Irish medical journal·2023
Same author

NOD1 mediates interleukin-18 processing in epithelial cells responding to Helicobacter pylori infection in mice.

Nature communications·2023
Same author

The effect of standing posture on amplitude and variability of postural tremor in Parkinson's disease.

Neuroscience letters·2023
Same journal

Electro-osmotic metachronal cilia transport of viscoelastic blood infused with penta-hybrid nanoparticles in an oviduct: Analytical and neural network modeling.

Computers in biology and medicine·2026
Same journal

sEEGnal: an automated EEG preprocessing pipeline evaluated against expert-driven preprocessing.

Computers in biology and medicine·2026
Same journal

Corrigendum to "Integrating experimental biology, computational methods, and artificial Intelligence in anticancer drug discovery: Bridging the translational Gap" [Comput. Biol. Med. 213 (2026) 111832].

Computers in biology and medicine·2026
Same journal

Organ dose optimization for a point-of-care forearm X-ray photon-counting CT.

Computers in biology and medicine·2026
Same journal

Physics-guided transformation of breathomic feature spaces into disease-specific representations for respiratory disease classification.

Computers in biology and medicine·2026
Same journal

An AI-driven deep learning pipeline for taxonomic classification and biodiversity assessment of deep-sea environmental DNA.

Computers in biology and medicine·2026
See all related articles

Choosing the right clustering method for gene expression data analysis is complex. This review offers a framework to evaluate clustering techniques, addressing limitations of common approaches for microarray data.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Gene expression data from microarray experiments is crucial for understanding biological processes.
  • Numerous clustering techniques exist, but selecting the most appropriate one for specific datasets remains challenging.
  • Existing methods often fail to account for the unique characteristics and profiles of gene expression data.

Purpose of the Study:

  • To review state-of-the-art clustering applications for gene expression analysis.
  • To address the limitations of current clustering approaches in handling microarray data.
  • To provide a framework for evaluating the effectiveness of clustering methods in this field.

Main Methods:

  • Survey of existing literature on clustering techniques for gene expression data.

Related Experiment Videos

  • Analysis of common limitations and challenges in applying these methods.
  • Discussion of the nature of microarray data and its implications for clustering.
  • Presentation of selected examples of clustering methods.

    • Main Results:

    • Identification of key limitations in standard clustering approaches for gene expression data.
    • Demonstration of how data profiles influence method performance.
    • Examples illustrating the application and evaluation of different clustering techniques.
    • A proposed framework for assessing clustering method suitability.

    Conclusions:

    • The selection of clustering methods for gene expression analysis requires careful consideration of data characteristics.
    • A systematic evaluation framework is essential for choosing appropriate techniques.
    • Addressing the limitations of common methods can improve the reliability of gene expression data analysis.