Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Flow Cytometry

Flow Cytometry

The development of flow cytometry techniques began in 1934 with initial attempts by Andrew Moldavan, a bacteriologist who counted the cells in a flowing capillary system. Moldavan pumped cells through a capillary tube focused under a microscope for visualization. The invention of photometry allowed the measurement of differentially-stained cells, and Louis Kamentsky developed the first multiparameter flow cytometer in 1965 to identify and count the cancer cells in cervical tissue specimens.
In...

Vector Algebra: Method of Components

Vector Algebra: Method of Components

It is cumbersome to find the magnitudes of vectors using the parallelogram rule or using the graphical method to perform mathematical operations like addition, subtraction, and multiplication. There are two ways to circumvent this algebraic complexity. One way is to draw the vectors to scale, as in navigation, and read approximate vector lengths and angles (directions) from the graphs. The other way is to use the method of components.
In many applications, the magnitudes and directions of...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Benchmarking the urinary transplant metabolome of kidney transplant recipients using healthy organ donors.

Frontiers in transplantation·2026

Same author

Machine learning based EMG analysis of intermuscular coherence and cumulant density in tremor and myoclonus.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology·2026

Same author

Toxicological impacts of environmentally equivalent microplastics and cadmium co-exposure in tropical freshwater crab <i>Sartoriana spinigera</i>.

Frontiers in toxicology·2026

Same author

Structure Is Information: Structural Identifiability Mappings for Machine Learning With Partially Observed Dynamical Systems.

IEEE transactions on cybernetics·2026

Same author

Untargeted Plasma Proteomic Signatures and Late Graft Failure in Kidney Transplant Recipients.

Transplantation·2026

Same author

Endocrine and metabolic determinants of cardiometabolic risk in mild autonomous cortisol secretion.

EBioMedicine·2026

Same journal

Invaders taking over-Mollusc faunal change in volcanic barrier lakes of the Albertine Rift biodiversity hotspot.

PloS one·2026

Same journal

AI-driven molecular diversification and ligand-based optimization of macitentan derivatives targeting VEGFR1 and endothelin signaling pathways.

PloS one·2026

Same journal

Performance patterns and records in the world aquatics masters championships: Where do the most frequently represented nations among the top-ten masters swimmers come from?

PloS one·2026

Same journal

Modeling diurnal Temperature-Rainfall relationships under multicollinearity using PLS-SEM: A case study of Ghana.

PloS one·2026

Same journal

Organizational culture, social capital, and emergency capacity in primary healthcare institutions: A cross-sectional structural equation modeling study comparing ordinary and older communities.

PloS one·2026

Same journal

Impact of kidney function on the metabolome in the general population.

PloS one·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 13, 2026

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Published on: December 12, 2019

Analysis of flow cytometry data by matrix relevance learning vector quantization.

Michael Biehl¹, Kerstin Bunte, Petra Schneider

¹Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, Groningen, The Netherlands. m.biehl@rug.nl

|March 26, 2013

Summary

This summary is machine-generated.

This study developed a machine learning approach using Generalized Matrix Relevance Learning Vector Quantization (GMLVQ) for analyzing flow cytometry data. The method accurately classified Acute Myeloid Leukemia (AML) patients, improving diagnostic accuracy.

More Related Videos

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

Multicolor Flow Cytometry-based Quantification of Mitochondria and Lysosomes in T Cells

Multicolor Flow Cytometry-based Quantification of Mitochondria and Lysosomes in T Cells

Published on: January 9, 2019

Related Experiment Videos

Last Updated: May 13, 2026

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Published on: December 12, 2019

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

Multicolor Flow Cytometry-based Quantification of Mitochondria and Lysosomes in T Cells

Multicolor Flow Cytometry-based Quantification of Mitochondria and Lysosomes in T Cells

Published on: January 9, 2019

Area of Science:

Computational biology
Machine learning in medicine
Hematology

Background:

Flow cytometry generates high-dimensional data crucial for disease diagnosis.
Automated computational approaches can enhance the analysis of flow cytometry data for clinical decision support.
Acute Myeloid Leukemia (AML) diagnosis benefits from precise cell population analysis.

Purpose of the Study:

To apply machine learning for automated classification of Acute Myeloid Leukemia (AML) using flow cytometry data.
To evaluate the performance of Generalized Matrix Relevance Learning Vector Quantization (GMLVQ) in AML diagnosis.
To investigate the relevance of specific markers and features for AML classification.

Main Methods:

Feature extraction from flow cytometry data using single marker statistics (moments, median, IQR).
Application of Generalized Matrix Relevance Learning Vector Quantization (GMLVQ), an adaptive machine learning technique.
Classification of patients into healthy or AML categories based on extracted features.

Main Results:

The GMLVQ approach achieved top performance in the DREAM6/FlowCAP2 challenge for AML diagnosis.
The method successfully classified test set patients, demonstrating high diagnostic accuracy.
Feature relevance analysis provided insights into key markers for AML identification.

Conclusions:

GMLVQ is a powerful tool for analyzing high-dimensional flow cytometry data in AML diagnostics.
Automated classification using GMLVQ can significantly aid in disease diagnosis and decision support.
The approach offers interpretability by identifying relevant diagnostic features.