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

An adaptive strategy for single- and multi-cluster gene assignment.

Sanjeev Garg1, Marc F Hansen, David W Rowe

  • 1Department of Chemical Engineering, University of Connecticut, Storrs, Connecticut 06269, USA.

Biotechnology Progress
|August 2, 2003
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

Erratum to "Porphyromonas gingivalis lipids enhance RANKL-mediated osteoclast formation" [Bone 207 (2026) 117852].

Bone·2026
Same author

Porphyromonas gingivalis lipids enhance RANKL-mediated osteoclast formation.

Bone·2026
Same author

Correction: Li et al. Genetic Deficiency of Hyaluronan Synthase 2 in the Developing Limb Mesenchyme Impairs Postnatal Synovial Joint Formation. <i>Biomedicines</i> 2025, <i>13</i>, 1324.

Biomedicines·2026
Same author

Genetic Deficiency of Hyaluronan Synthase 2 in the Developing Limb Mesenchyme Impairs Postnatal Synovial Joint Formation.

Biomedicines·2025
Same author

Inhibition and Degradation of Amyloid Beta Fibrils by Peptide Inhibitors.

The journal of physical chemistry. B·2025
Same author

Non-Nucleoside Lycorine-Based Analogs as Potential DENV/ZIKV NS5 Dual Inhibitors: Structure-Based Virtual Screening and Chemoinformatic Analysis.

Metabolites·2024
Same journal

Deep dive into drug substance HCP patterns for comprehensive safety assessment in recombinant mAb formats.

Biotechnology progress·2026
Same journal

Application of commercial ultrafiltration membranes for protein purification by high performance countercurrent membrane purification (HPCMP).

Biotechnology progress·2026
Same journal

Demonstration of nanofiltration-based virus safety for AAV manufacturing using model viruses.

Biotechnology progress·2026
Same journal

Purification and concentration of model viruses using single-pass tangential flow filtration.

Biotechnology progress·2026
Same journal

Advanced glucose control strategies leveraging Raman spectroscopy for optimized mammalian cell culture manufacturing.

Biotechnology progress·2026
Same journal

Mechanistic deconvolution of BSA size variants by constrained Raman pseudo-Voigt hard modeling during anion-exchange chromatography.

Biotechnology progress·2026
See all related articles

The adaptive centroid algorithm (ACA) improves gene clustering by analyzing expression patterns, including negative correlations, to identify functional gene groups. This novel approach offers unique solutions and enhances pathway analysis in bioinformatics.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Current gene clustering algorithms have limitations, including strict gene assignment and reliance on positive correlations.
  • Genes with negative correlations may share functions and pathway involvement, which existing methods often overlook.

Purpose of the Study:

  • To introduce the adaptive centroid algorithm (ACA) for gene expression data clustering.
  • To address inadequacies in existing algorithms by incorporating negative correlations and ensuring unique solutions.

Main Methods:

  • The ACA utilizes a two-way analysis of variance (ANOVA) on gene expression matrices, with gene expression and experimental condition as factors.
  • It employs a performance criterion based on the residual mean squared error (MSE) from ANOVA.

Related Experiment Videos

  • Euclidean distances and the center-of-mass principle are used, and clusters are identified using Pearson correlation coefficients.

    • Main Results:

    • The ACA provides unique solutions for gene clustering.
    • Validation using ANOVA shows significantly lower MSEs for ACA-derived clusters compared to the original data.
    • The algorithm successfully performs both single- and multi-cluster gene assignments.

    Conclusions:

    • The adaptive centroid algorithm (ACA) offers a robust method for gene clustering, overcoming limitations of traditional approaches.
    • By considering negative correlations and using an ANOVA-based criterion, ACA enhances the identification of functionally related genes and pathways.
    • ACA provides unique and validated cluster assignments, improving the analysis of gene expression data.