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

A novel strategy for microarray quality control using Bayesian networks.

Sampsa Hautaniemi1, Henrik Edgren, Petri Vesanen

  • 1Institute of Signal Processing, Tampere University of Technology, PO Box 553, 33101 Tampere, Finland. sampsa.hautaniemi@tut.fi

Bioinformatics (Oxford, England)
|November 5, 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

Mapping the future of medicine through digital twins.

Frontiers in molecular medicine·2026
Same author

A multi-center study on the consistency of drug sensitivity testing in patients with acute myeloid leukemia.

NPJ precision oncology·2026
Same author

Midostaurin response in AML is shaped by a progenitor-like cell state selectively targeted by SMAC mimetics.

NPJ precision oncology·2026
Same author

Landscape of circular RNAs in acute myeloid leukemia and their clinical significance.

NPJ precision oncology·2026
Same author

Tumor cell FAP orchestrates EMT and immune suppression in aggressive localized ccRCC.

Theranostics·2026
Same author

Validation of fibroblast activation protein and α-smooth muscle actin as prognostic biomarkers in prostate cancer through AI-assisted image analysis of dual-marker IHC.

The journal of pathology. Clinical research·2025
Same journal

Biomedical Concept Recognition with Error-aware Negative-enhanced Ranking Framework.

Bioinformatics (Oxford, England)·2026
Same journal

TEDLH: Domain HMMs for sensitive detection of remote homologues.

Bioinformatics (Oxford, England)·2026
Same journal

PLNFGL: Joint Estimation of Multi-Condition Gene Networks from Single-cell RNA-seq Data.

Bioinformatics (Oxford, England)·2026
Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026
Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026
Same journal

EDEL: Enhancing Dense Retrievers for Curation of Biomedical Knowledge Bases.

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

This study introduces a Bayesian network approach for microarray spot quality control. This method reliably identifies and removes low-quality data points, improving the accuracy of gene expression analysis.

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • High-throughput microarray technology measures thousands of gene expression levels simultaneously.
  • Variability in microarray printing, hybridization, and washing can compromise data quality.
  • Erroneous measurements can distort normalization and lead to incorrect biological conclusions.

Purpose of the Study:

  • To develop a reliable and general strategy for microarray spot quality control.
  • To automate the identification and removal of low-quality microarray data points.
  • To enhance the accuracy of gene expression analysis from microarray experiments.

Main Methods:

  • Utilizing Bayesian networks for microarray spot quality assessment.
  • Employing a non-linear least squares-based Gaussian fitting procedure to extract spot features.

Related Experiment Videos

  • Analyzing features including spot intensity, size, roundness, alignment error, background intensity, background noise, and bleeding.

    • Main Results:

    • Demonstrated the effectiveness of Bayesian networks in microarray spot quality control.
    • Successfully extracted key features from microarray spots for quality assessment.
    • Validated Bayesian networks as a robust tool for identifying unreliable microarray data.

    Conclusions:

    • Bayesian networks offer a reliable and effective model for microarray spot quality assessment.
    • Automated quality control is essential for accurate gene expression analysis.
    • This strategy addresses the challenge of manual quality control in large-scale microarray experiments.