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

Multi-way clustering of microarray data using probabilistic sparse matrix factorization.

Delbert Dueck1, Quaid D Morris, Brendan J Frey

  • 1Department of Electrical and Computer Engineering, University of Toronto Toronto, Ontario, Canada M5S 3G4. delbert@psi.toronto.edu

Bioinformatics (Oxford, England)
|June 18, 2005
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

An X-linked long non-coding RNA, PTCHD1-AS, and the core features of autism.

Nature·2026
Same author

Orthrus: toward evolutionary and functional RNA foundation models.

Nature methods·2026
Same author

CisBP-RNA: a web resource for eukaryotic RNA-binding proteins and their motifs.

Nucleic acids research·2025
Same author

Orthrus: Towards Evolutionary and Functional RNA Foundation Models.

bioRxiv : the preprint server for biology·2024
Same author

Crowd-sourced benchmarking of single-sample tumor subclonal reconstruction.

Nature biotechnology·2024
Same author

SON is an essential m<sup>6</sup>A target for hematopoietic stem cell fate.

Cell stem cell·2023
Same journal

3DICE: Interpretable 3D Cross-Modal Learning for Drug-Target Interaction Prediction and Large-Scale Drug Discovery.

Bioinformatics (Oxford, England)·2026
Same journal

KASSPer: Kinase Active Site Structure Prediction using Protein and Ligand Language Models and Its Application to Virtual Screening.

Bioinformatics (Oxford, England)·2026
Same journal

IDR searcher: a search engine solution for public image resources.

Bioinformatics (Oxford, England)·2026
Same journal

KCFtools: Rapid alignment-free method for introgression screening and GWAS using k-mer profiles.

Bioinformatics (Oxford, England)·2026
Same journal

Meta2DB: Curated shotgun metagenomic feature sets and metadata for health state prediction.

Bioinformatics (Oxford, England)·2026
Same journal

conMItion: an R package adjusting confounding factors for associations in multi-omics.

Bioinformatics (Oxford, England)·2026
See all related articles

This study introduces probabilistic sparse matrix factorization (PSMF) for multi-way clustering of microarray data. PSMF improves functional clustering of mRNA expression data by handling noise and uncertainty, outperforming standard methods.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Microarray data analysis presents challenges in multi-way clustering.
  • Existing methods may struggle with noise and uncertainty in biological data.

Purpose of the Study:

  • To develop a robust generative model for multi-way clustering of microarray data.
  • To introduce probabilistic sparse matrix factorization (PSMF) as an advancement over hard-decision algorithms.

Main Methods:

  • Utilized a probabilistic generative model approach.
  • Developed probabilistic sparse matrix factorization (PSMF).
  • PSMF accounts for sensor noise and uncertainty in hidden prototypes and data assignments.

Main Results:

Related Experiment Videos

  • PSMF demonstrated superior recovery of functionally-relevant clusterings in mRNA expression data.
  • Outperformed standard clustering techniques like hierarchical agglomerative clustering.
  • Probabilistic computation prevented convergence to suboptimal solutions compared to point estimates.

Conclusions:

  • PSMF offers a more reliable approach to multi-way clustering of gene expression data.
  • The probabilistic nature of PSMF enhances robustness against data imperfections.
  • This method improves the biological interpretability of clustering results.