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.
Authors
- Anupama E. Gururaj (ORCID: https://orcid.org/0000-0002-4221-4379)
- Lili Wang (ORCID: https://orcid.org/0000-0003-2456-898X)
- Sheng Lin‐Gibson (ORCID: https://orcid.org/0000-0001-5092-1519)
- Dawei Lin (ORCID: https://orcid.org/0000-0002-5506-0030)
Institutions
- National Institute of Standards and Technology (US)
- National Institute of Allergy and Infectious Diseases (US)
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