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

Mixture model analysis of DNA microarray images.

K Blekas1, N P Galatsanos, A Likas

  • 1Department of Computer Science, University of Ioannina, 45110 Ioannina, Greece. kblekas@cs.uoi.gr

IEEE Transactions on Medical Imaging
|July 14, 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

Regularized total least squares approach for nonconvolutional linear inverse problems.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2008
Same author

Hierarchical Bayesian image restoration from partially known blurs.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2008
Same author

Artificial neural networks for solving ordinary and partial differential equations.

IEEE transactions on neural networks·2008
Same author

Neural-network methods for boundary value problems with irregular boundaries.

IEEE transactions on neural networks·2008
Same author

A novel method for automated EMG decomposition and MUAP classification.

Artificial intelligence in medicine·2005
Same author

Characterization of clustered microcalcifications in digitized mammograms using neural networks and support vector machines.

Artificial intelligence in medicine·2005

This study introduces a novel method for microarray image analysis using a Gaussian mixture model (GMM) for spot analysis and a new gridding algorithm. The approach enhances accuracy by detecting and compensating for image artifacts.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Image Analysis

Background:

  • Microarray technology is crucial for gene expression studies.
  • Accurate analysis of microarray images is essential for reliable biological insights.
  • Existing methods may struggle with image artifacts and adaptability.

Purpose of the Study:

  • To propose a novel, flexible, and adaptive methodology for microarray image analysis.
  • To improve the accuracy of individual spot detection and analysis in microarray images.
  • To develop a robust approach capable of handling and correcting image artifacts.

Main Methods:

  • A new gridding algorithm for precise spot and border determination.
  • Application of a Gaussian mixture model (GMM) for individual spot image analysis.

Related Experiment Videos

  • Utilizing maximum likelihood and maximum a posteriori estimations with the expectation-maximization algorithm for GMM parameter estimation.

    • Main Results:

    • The proposed GMM approach demonstrates flexibility and adaptability to microarray data.
    • The methodology effectively detects and compensates for artifacts in microarray images.
    • Numerical experiments show advantages over previous methods and existing software tools.

    Conclusions:

    • The novel methodology offers a robust and accurate solution for microarray image analysis.
    • The GMM-based approach provides superior modeling flexibility and artifact compensation.
    • This technique enhances the reliability of gene expression data derived from microarrays.