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

Using a neural network with flow cytometry histograms to recognize cell surface protein binding patterns.

Eun-Young Kim1, Qing Zeng, James Rawn

  • 1Decision Systems Group, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA.

Proceedings. AMIA Symposium
|December 5, 2002
PubMed
Summary

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

Evaluation of immune response in calves vaccinated with baculovirus vectored inactivated vaccine expressing 2.3.4.4b H5 protein of highly pathogenic Avian Influenza virus.

Veterinary microbiology·2026
Same author

Novel recombinant H5-based vaccine provides effective protection against H5N1 influenza virus in cats.

NPJ vaccines·2026
Same author

Immunomodulation of Pluripotent Stem Cell-Derived Mesenchymal Stem Cells in Rotator Cuff Tears Model.

Biomedicines·2022
Same author

A Meta-Synthesis Study of Person-Centered Care Experience from the Perspective of Nursing Home Residents.

International journal of environmental research and public health·2022
Same author

Orthogonal assay for validation of Tox21 PPARγ data and applicability to in silico prediction model.

Toxicology in vitro : an international journal published in association with BIBRA·2022
Same author

Association of Carbapenemase-Producing <i>Enterobacterales</i> Detected in Stream and Clinical Samples.

Frontiers in microbiology·2022

A neural network approach effectively analyzes flow cytometry data by comparing antibody-antigen binding patterns. This method achieves high accuracy in identifying identical or similar molecules, enhancing flow cytometry data analysis.

Area of Science:

  • Biological Sciences
  • Medicine
  • Immunology

Background:

  • Flow cytometry is a widely used technique in biological and medical research.
  • Identifying unknown molecules, like antibodies binding to specific antigens, is crucial for biological analysis.

Purpose of the Study:

  • To develop an analytical tool for flow cytometry data comparison using neural networks.
  • To identify unknown molecules by recognizing antibody-antigen binding patterns.

Main Methods:

  • Extracted peak locations from flow cytometry histograms.
  • Utilized Marquardt backpropagation neural networks for data comparison.
  • Classified antibody-antigen binding patterns based on peak locations.

Main Results:

Related Experiment Videos

  • The neural network achieved high correct classification rates, ranging from 93.8% to 99.6%.
  • The system accurately identified identical or similar molecular binding patterns.
  • Demonstrated the effectiveness of neural networks in flow cytometry data analysis.

Conclusions:

  • Neural network analysis is a valuable tool for flow cytometry histogram data.
  • This technique can accurately identify and classify molecular interactions.
  • Supports the advancement of analytical methods in biological and medical research.