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Enhancing Access and Reproducibility of Flow Data Analysis through Machine Learning-Based Automated Cross-Panel Classification at Sample and Cell Level
Conference paper

Enhancing Access and Reproducibility of Flow Data Analysis through Machine Learning-Based Automated Cross-Panel Classification at Sample and Cell Level

Yu-Fen Wang, En-Ping Chu, Yi-Hsuan Li, Guo-Hung Li, 奐宇 陳, Joseph Hanson, Joseph D. Tario, Kai-Fu, 祈均 李, Sara A Monaghan, …
European Society for Clinical Cell Analysis (ESCCA)
2024

Abstract

Flow Cytometry;Machine Learning
<p>&nbsp;</p><ul> <li style="text-align: justify;"><meta charset="UTF-8" />The lack of standardization in panel design and the complexity of manual analysis for flow cytometry (FC) data have hindered the speed and the spread of both implementation and the service capacity of flow cytometry being in the emerging countries.</li> <li style="text-align: justify;">This study employs multiple machine learning (ML) approaches to develop an automated panel-agnostic AML classification at both sample and cell levels from several distinct flow cytometry laboratories.</li></ul>

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