AI and flow cytometry

Artificial intelligence (AI) and machine learning (ML) are transforming biotechnology and playing a key role in bioeconomy. One of the most important measurement capabilities at the forefront of biotechnology innovations is flow cytometry (FCM), a high-throughput, single-cell analysis platform technology. However, the quality and consistency of FCM data can vary significantly across laboratories and study datasets, resulting in millions of FCM datasets siloed for their use in AI applications. This workshop focuses on overcoming challenges and identifying solutions that include essential measurements, reference controls, AI-ready reference data, and current AI/ML models. It aims to advance AI/ML applications in FCM and related data.

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Publication Details

Journal
The Journal of Immunology
Published
2025-11-10
DOI
https://doi.org/10.1093/jimmun/vkaf292
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
0.00
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article

AI and flow cytometry

Anupama E. Gururaj, Lili Wang, Sheng Lin‐Gibson, Dawei Lin
The Journal of Immunology
Single-cell and spatial transcriptomics
article

AI and flow cytometry

Anupama E. Gururaj, Lili Wang, Sheng Lin‐Gibson, Dawei Lin
article en

Abstract

Artificial intelligence (AI) and machine learning (ML) are transforming biotechnology and playing a key role in bioeconomy. One of the most important measurement capabilities at the forefront of biotechnology innovations is flow cytometry (FCM), a high-throughput, single-cell analysis platform technology. However, the quality and consistency of FCM data can vary significantly across laboratories and study datasets, resulting in millions of FCM datasets siloed for their use in AI applications. This workshop focuses on overcoming challenges and identifying solutions that include essential measurements, reference controls, AI-ready reference data, and current AI/ML models. It aims to advance AI/ML applications in FCM and related data.

The Journal of ImmunologyVol. 215(2)
National Institute of Standards and Technology (US), National Institute of Allergy and Infectious Diseases (US)
Openalex Percentile: Top 70%
Single-cell and spatial transcriptomics
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