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

Informatics and quantitative analysis in biological imaging.

Jason R Swedlow1, Ilya Goldberg, Erik Brauner

  • 1Division of Gene Regulation and Expression, Wellcome Trust Biocentre, University of Dundee, Dow Street, Dundee DD1 5EH, Scotland. j.swedlow@dundee.ac.uk

Science (New York, N.Y.)
|April 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

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

Bioinformatics (Oxford, England)·2026
Same author

Restraint of melanoma progression by cells in the local skin environment.

eLife·2026
Same author

Systematic characterisation of site-specific proline hydroxylation using hydrophilic interaction chromatography and mass spectrometry.

eLife·2026
Same author

PHD1-dependent hydroxylation of RepoMan (CDCA2) on P604 modulates the control of mitotic progression.

eLife·2026
Same author

Compositional and interpretable representation of histology using AI foundation models and sparse autoencoders.

bioRxiv : the preprint server for biology·2026
Same author

Modeling development of tertiary lymphoid structures in pulmonary tuberculosis by 3D profiling and trajectory analysis.

bioRxiv : the preprint server for biology·2026
Same journal

Erratum for the Research Article "Detecting supramolecular organic nanoparticles during heat wave".

Science (New York, N.Y.)·2026
Same journal

Local signals, systemic decline.

Science (New York, N.Y.)·2026
Same journal

The mechanics of liver regeneration.

Science (New York, N.Y.)·2026
Same journal

Computing in a memory with physics.

Science (New York, N.Y.)·2026
Same journal

Retraction.

Science (New York, N.Y.)·2026
Same journal

Making time.

Science (New York, N.Y.)·2026
See all related articles

The Open Microscopy Environment (OME) offers a solution for analyzing biological images. This informatics tool aims to automate the analysis of large image datasets for cell biology research.

Area of Science:

  • Biology
  • Bioinformatics
  • Microscopy

Background:

  • Biological imaging is a quantitative method for studying cell structure and dynamics.
  • Cell-based screens increasingly rely on digital image analysis.
  • Current bioinformatics tools for hypothesis-driven image analysis are underdeveloped.

Purpose of the Study:

  • To develop an informatics solution for storing and analyzing optical microscope image data.
  • To create the Open Microscopy Environment (OME) for biological image analysis.
  • To address the immaturity of bioinformatics tools in biological image informatics.

Main Methods:

  • Developing a flexible data model for biological images.
  • Implementing a relational database for image data storage.

Related Experiment Videos

  • Specifying an XML-encoded file standard for broad software compatibility.

    • Main Results:

    • The Open Microscopy Environment (OME) provides a framework for biological image data management.
    • OME facilitates automated image analysis, modeling, and data mining.
    • The OME design supports integration with various software tools.

    Conclusions:

    • The Open Microscopy Environment (OME) is a foundational step toward advanced biological image informatics.
    • OME standardizes biological image data, enabling more robust analysis.
    • This approach supports hypothesis-driven research using large-scale microscopy datasets.