RT journal article
T1 Automatic computational classification of bone marrow cells for B cell pediatric leukemia using UMAP
A1 Niño López, Ana del Rosario
A1 Martínez Rubio, Álvaro
A1 Picón González, Rocío
A1 Castillo Robleda, Ana
A1 Ramírez Orellana, Manuel
A1 Chulian García, Salvador
A1 Rosa Durán, María
A2 Matemáticas
K1 Acute leukemia
K1 Automation
K1 UMAP
K1 Flow cytometry
AB B Acute Lymphoblastic Leukemia (B-ALL) accounts for approximately 80% of pediatric leukemia cases. Despite treatment advances, 15–20% of children experience relapse, highlighting the need of improved monitoring of patients and novel strategies leading to successful therapies. Flow Cytometry is an essential technique for measuring residual disease and guiding treatment. However, traditional manual gating limits its efficiency. In recent years, computational tools have been integrated to enhance these clinical processes but many mathematical techniques are underexploited. Particularly, Uniform Manifold Approximation and Projection (UMAP), together with Machine Learning, provide promising approaches for analyzing large datasets. Mathematical tools and artificial intelligence offer new perspectives on these health problems, beyond the usual approach in biomedicine. We have exploited 234 samples from 75 B-ALL patients to develop an artificial intelligence-based algorithm that can improve patient classification and therapy decisions in different patient cohorts. This implies an advancement on the routine manual analysis of the disease progression, as we identify key subpopulations automatically, distinguishing patients’ bone marrow regeneration patterns, thus improving the prediction and prognosis of the disease.
PB BioMed Central Ltd
SN 1756-0381
YR 2025
FD 2025
LK http://hdl.handle.net/10498/39591
UL http://hdl.handle.net/10498/39591
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 21-sep-2026