一致性(知识库)
流式细胞术
钥匙(锁)
计算机科学
质量(理念)
计算生物学
人工智能
细胞仪
流量(数学)
数据挖掘
数据科学
生物
数据质量
作者
Dawei Lin,Anupama Gururaj,Sheng Lin-Gibson,Lili Wang
标识
DOI:10.1093/jimmun/vkaf292
摘要
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.
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