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

LS-NMF: a modified non-negative matrix factorization algorithm utilizing uncertainty estimates.

Guoli Wang1, Andrew V Kossenkov, Michael F Ochs

  • 1Division of Population Science, Fox Chase Cancer Center, Philadelphia, PA, USA. guoli.wang@fccc.edu

BMC Bioinformatics
|March 30, 2006
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

Nuclear export of R-loop by the DDX1 and XPO1 complex promotes senescence-associated secretory phenotype and inflammaging.

Nature aging·2026
Same author

Effects of the traditional nine steaming and nine sun-drying on the bioactivity of traditional Chinese medicine.

Journal of ethnopharmacology·2026
Same author

Thiol-Isocyanate Photoprinting of Liquid Crystal Elastomers Toward High Toughness and Reprintability.

Angewandte Chemie (International ed. in English)·2026
Same author

Effects of oil types on the stability of oil-in-water emulsions prepared using Dioscorea opposita mucilage and gums.

Food chemistry·2026
Same author

Dynamic assessment of gastric emptying patterns after endoscopic bowel preparation using ultrasonography: A prospective observational study.

Journal of clinical anesthesia·2026
Same author

Mitochondrial double-stranded RNA fuels pancreatic cancer growth via RIG-I/TLR3 inflammation.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

SNPio: a Python interface for population genomic data processing.

BMC bioinformatics·2026
Same journal

SpaHNR: a spatial domain identification method via sparse attention-based hierarchical node representation and multi-view contrastive learning.

BMC bioinformatics·2026
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026
Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026
Same journal

SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint.

BMC bioinformatics·2026
See all related articles

Least squares non-negative matrix factorization (LS-NMF) improves gene expression analysis by incorporating uncertainty measurements. This enhanced machine learning approach significantly improves the identification of functionally related genes from microarray data.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Machine Learning

Background:

  • Non-negative matrix factorization (NMF) is a machine learning algorithm used for analyzing microarray data.
  • NMF identifies patterns by linearly combining expression signatures but does not utilize gene-specific uncertainty estimates.
  • Gene expression uncertainty data is known to be valuable for pattern recognition.

Purpose of the Study:

  • To develop a novel algorithm that integrates gene expression uncertainty measurements into NMF.
  • To improve the performance of NMF in identifying functionally related genes.

Main Methods:

  • Developed a new algorithm: least squares non-negative matrix factorization (LS-NMF).
  • LS-NMF incorporates uncertainty measurements into the standard NMF updating rules.

Related Experiment Videos

  • Evaluated LS-NMF's performance against standard NMF using MIPS database annotations.

    • Main Results:

    • LS-NMF maintains the advantages of NMF, including ease of implementation and guaranteed local optimum.
    • LS-NMF demonstrates significantly improved performance in linking functionally related genes compared to standard NMF.
    • The integration of uncertainty measurements enhances the power of NMF for gene expression data analysis.

    Conclusions:

    • Uncertainty measurements in gene expression data offer critical insights for analysis.
    • The LS-NMF algorithm effectively leverages this uncertainty information to enhance NMF's capabilities.
    • LS-NMF represents a significant advancement in pattern recognition for gene expression data analysis.