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Experiment Overview

Repository ID: FR-FCM-Z7A2 Experiment name: Relapse prediction in leukemia MIFlowCyt score: 32.19%
Primary researcher: Álvaro Martínez-Rubio PI/manager: Álvaro Martínez-Rubio Uploaded by: Álvaro Martínez-Rubio
Experiment dates: 2023-01-01 - 2024-04-12 Dataset uploaded: Apr 2024 Last updated: Apr 2024
Keywords: [Classification] [acute lymphoblastic leukemia] [relapse] [machine] Manuscripts:
Organizations: University of Cádiz, Mathematics, Puerto Real, Cádiz (Spain)
Purpose: The goal of the study was to predict wether flow cytometry data at diagnosis could improve risk stratification in children with B-cell Acute Lymphoblastic Leukemia.
Conclusion: Flow cytometry data, as characterized by the profile of intensity of expression, is unable to predicte relapse. Alternative characterizations are required to keep exploring this research direction.
Comments: In this study we characterize the marker expression by summarizing each marker's intensity of expression with basic statistical moments: Mean, median, standard deviation, skewness and kurtosis. We feed this information into a classifier to obtain an estimate of its predictive capacity. Code and patient annotations available at https://github.com/Almr95/Relapse-Prediction
Funding: This work was partially supported by project PDC2022-133520-I00 funded by Ministerio de Ciencia e Innovación/ Agencia Estatal de investigación (doi:10.13039/501100011033) and European Union NextGenerationEU/PRTR; by project PID2022-140451OA-I00 funded by Ministerio de Ciencia e Innovación/Agencia Estatal de investigación (doi:10.13039/501100011033) and ERDF A way of making Europe; and by University of Castilla-La Mancha / ERDF, A way of making Europe (Applied Research Projects) under grant 2022-GRIN-34405. The support of Fundación Española para la Ciencia y la Tecnología (FECYT project PR214), Asociación Pablo Ugarte (APU, Spain) and Junta de Andalucía (Spain) group FQM-201 is also acknowledged. This work was also subsidized in its early stages by a grant for the research and biomedical innovation in the health sciences within the framework of the Integrated Territorial Initiative (ITI) for the province of Cadiz (grant number ITI-0038-2019).
Quality control: Standard quality control for FACsCanto II. Manual and computational preprocessing was performed (doublets, debris, margin events, acquisition errors, batch effects)
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