Lin, D.
, Gururaj, A.
, Lin-Gibson, S.
and Wang, L.
(2025),
AI and Flow Cytometry, Journal of Immunology, [online], https://doi.org/10.1093/jimmun/vkaf292, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=961141
(Accessed November 20, 2025)
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AI and Flow Cytometry
Published
Author(s)
Dawei Lin, Anupama Gururaj, ,
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.Citation
Journal of Immunology
Pub Type
Journals
Download Paper
Keywords
flow cytometry, FCM, artificial intelligence, AI, machine learning, ML, immunology, diagnostics
Citation
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Created November 10, 2025, Updated November 17, 2025