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

Microarray data clustering based on temporal variation: FCV with TSD preclustering.

Carla S Möller-Levet1, Kwang-Hyun Cho, Olaf Wolkenhauer

  • 1Department of Electrical Engineering and Electronics, Control Systems Centre, University of Manchester Institute of Science and Technology (UMIST), Manchester, UK.

Applied Bioinformatics
|May 8, 2004
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

ZW4864-mediated inhibition of the β-catenin/BCL9/BCL9L complex reveals therapeutic potential in bladder cancer.

Molecular oncology·2026
Same author

NetCF: A Network Control-based Framework to Reveal the Molecular Mechanism of Phenotype Switching in Lung Cancer.

Genomics, proteomics & bioinformatics·2026
Same author

A Realistic Control Approach for Set Stabilization of Complex Biological Networks With Logical Models.

IEEE transactions on computational biology and bioinformatics·2026
Same author

Intertwining of the IGF system and animal welfare.

Scientific reports·2026
Same author

A Rho GTPase-effector ensemble governs cell migration behavior.

Nature communications·2025
Same author

Exploring the Therapeutic Potential of <i>Moringa oleifera</i> Against Lung Cancer Through Network Modeling and Molecular Docking Analysis.

International journal of molecular sciences·2025
Same journal

Statistically consistent identification of differentially expressed genes in DNA chip data over the whole expression range: relative variance method.

Applied bioinformatics·2006
Same journal

A nonparametric likelihood ratio test to identify differentially expressed genes from microarray data.

Applied bioinformatics·2006
Same journal

Simulation study of ratio calculation formulae of two-colour cDNA microarray data.

Applied bioinformatics·2006
Same journal

Alternative mRNA polyadenylation can potentially affect detection of gene expression by affymetrix genechip arrays.

Applied bioinformatics·2006
Same journal

Comparisons of annotation predictions for affymetrix GeneChips.

Applied bioinformatics·2006
Same journal

Ontology annotation treebrowser : an interactive tool where the complementarity of medical subject headings and gene ontology improves the interpretation of gene lists.

Applied bioinformatics·2006
See all related articles

A new fuzzy c-varieties clustering with transitional state discrimination preclustering (FCV-TSD) algorithm effectively characterizes temporal gene expression patterns. This method outperforms k-means clustering for short time-series gene expression data analysis.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Conventional clustering algorithms like k-means and hierarchical clustering often fail to capture temporal dynamics in gene expression data.
  • Analyzing temporal relationships is crucial for understanding gene regulation and biological processes over time.

Purpose of the Study:

  • To introduce a novel clustering algorithm, fuzzy c-varieties clustering with transitional state discrimination preclustering (FCV-TSD), designed for short time-series gene expression data.
  • To enable the characterization of temporal relations within the clustering environment, a capability lacking in existing methods.

Main Methods:

  • The FCV-TSD algorithm employs a two-step approach to identify groups of data points exhibiting linear configurations in the data-space.

Related Experiment Videos

  • These configurations correspond to similar temporal expression patterns, effectively capturing time-domain similarities.
  • Validation involved artificial and real experimental datasets, with comparisons against k-means and random clustering.

    • Main Results:

    • FCV-TSD demonstrated superior performance compared to k-means clustering on both artificial and real datasets.
    • Performance was evaluated using internal cluster correlation and geometrical cluster properties.
    • The algorithm successfully identified groups with ordered temporal relationships.

    Conclusions:

    • The FCV-TSD algorithm offers an effective solution for clustering short time-series gene expression data by incorporating temporal dynamics.
    • It provides a more accurate representation of gene expression patterns over time than conventional methods.
    • This advancement has implications for understanding gene function and regulatory networks.