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

Neural network analysis of flow cytometry immunophenotype data

R Kothari1, H Cualing, T Balachander

  • 1Department of Electrical and Computer Engineering and Computer Science, University of Cincinnati, OH 45221-0030, USA. ravi.kothari@uc.edu

IEEE Transactions on Bio-Medical Engineering
|August 1, 1996
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

Particle composition and morphology over urban environment (New Delhi): Plausible effects on wheat leaves.

Environmental research·2021
Same author

Recurrent erythema nodosum leprosum associated with Mycobacterium indicus pranii vaccine in a case of leprosy: a rare paradox.

Journal of the European Academy of Dermatology and Venereology : JEADV·2021
Same author

Use of polypropylene mesh in contaminated and dirty strangulated hernias: short-term results.

Hernia : the journal of hernias and abdominal wall surgery·2018
Same author

Rechallenge temozolomide in glioma: A case series from India.

Indian journal of cancer·2017
Same author

Patterns of care and outcomes for second-line targeted therapy in metastatic renal cell carcinomas: A registry based analysis.

Indian journal of cancer·2017
Same author

Phycoremediation of textile wastewater by unicellular microalga Chlorella pyrenoidosa.

Cellular and molecular biology (Noisy-le-Grand, France)·2014
Same journal

Enhancing Volumetric Imaging in Linear-Array Photoacoustic Tomography: multiview fusion with deep learning.

IEEE transactions on bio-medical engineering·2026
Same journal

Robust Rule-based Heuristic Assistance Strategy for a Semi-Active Shoulder Exoskeleton Used in Overhead Work.

IEEE transactions on bio-medical engineering·2026
Same journal

Highly Accelerated 1-mm Isotropic 3D Chemical Exchange Saturation Transfer MRI Using Wave-Co-CAIPI at 5 Tesla.

IEEE transactions on bio-medical engineering·2026
Same journal

Systematic Evaluation of Hip Exoskeleton Assistance Parameters for Enhancing Gait Stability During Ground Slip Perturbations.

IEEE transactions on bio-medical engineering·2026
Same journal

SleepConFormer: A Single-Channel EEG Framework for Sleep Staging and Consciousness Assessment in Patients with Disorders of Consciousness.

IEEE transactions on bio-medical engineering·2026
Same journal

Modeling Partial and Total Support of Left Ventricular Assist Device for Discrete Hemodynamic Control Framework.

IEEE transactions on bio-medical engineering·2026
See all related articles

This study uses neural networks to classify acute leukemia subtypes based on cell antigens detected via flow cytometry. This objective classification aids in determining prognosis and treatment strategies for leukemia patients.

Area of Science:

  • Hematology
  • Immunology
  • Computational Biology

Background:

  • Acute leukemia is a significant cause of cancer mortality in the US.
  • Leukemia classification relies on cell phenotype, impacting treatment protocols and patient prognosis.
  • Subtyping based on differentiation and maturity influences treatment response and survival.

Purpose of the Study:

  • To apply immunophenotype flow cytometry data for leukemia categorization.
  • To classify leukemia into subcategories based on lineage and differentiation antigen expression using a neural classifier.

Main Methods:

  • Utilized 28 input features from flow cytometry data (mean fluorescence intensity of up to 27 antibodies) and a binary input for prior leukemia diagnosis.
  • Employed a feed forward neural network trained with backpropagation for classification.

Related Experiment Videos

  • Incorporated weight decay as a complexity regulation term to enhance generalization performance.

    • Main Results:

    • Achieved 0.0% training error and 10.3% generalization error for lineage categorization.
    • Achieved 0.0% training error and 10.0% generalization error for differentiation categorization.
    • Demonstrated the effectiveness of the neural classifier in objective phenotype analysis.

    Conclusions:

    • Objective classification of complex leukemia phenotypes is feasible using multiparameter flow cytometry data.
    • This approach facilitates categorization into prognostic subtypes, improving clinical decision-making.
    • Neural network analysis of immunophenotype data offers a powerful tool for leukemia research and patient care.