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

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

Yu-Fen Wang, Yi-Hsuan Li, 祈均 李, Guo-Hung Li, 奐宇 陳, Joseph Hanson, Joseph D. Tario, Kai-Fu, Sara A Monaghan, Paul Wallace, …
International Society for Advancement of Cytometry (ISAC) - CYTO 2024
2024

Abstract

Flow Cytometry;Machine Learning
<ul> <li style="text-align: justify;"><meta charset="UTF-8" />There is considerable variation among cytometry reagents, instruments and analysis approaches used for hematological disease diagnosis and monitoring.</li> <li style="text-align: justify;">We previously demonstrated that our approach is adaptable across different instruments for sample level classification model development.</li> <li style="text-align: justify;">In this study, we combined three different panels: ClearLLab10C, Euroflow AML/MDS and a UPMC diagnostic panel to demonstrate the feasibility of developing sample and cell level classification models thats instrument and reagent agnostic.</li></ul>

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