Refining risk prediction in pediatric acute lymphoblastic leukemia through DNA methylation profiling

Refining risk prediction in pediatric acute lymphoblastic leukemia through DNA methylation profiling

Abstract Acute lymphoblastic leukemia (ALL) is the most prevalent cancer in children, and despite considerable progress in treatment outcomes, relapses still pose significant risks of mortality and long-term complications. To address this challenge, we employed a supervised machine learning techniqu...

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Bibliographic Details
Main Authors: Adrián Mosquera Orgueira, Olga Krali, Carlos Pérez Míguez, Andrés Peleteiro Raíndo, José Ángel Díaz Arias, Marta Sonia González Pérez, Manuel Mateo Pérez Encinas, Manuel Fernández Sanmartín, Daniel Sinnet, Mats Heyman, Gudmar Lönnerholm, Ulrika Norén-Nyström, Kjeld Schmiegelow, Jessica Nordlund
Format: Article
Language:English
Published: BMC 2024-03-01
Series:Clinical Epigenetics
Subjects:
Pediatric acute lymphoblastic leukemia
Epigenetics
DNA methylation
Machine learning
Artificial intelligence
Relapse risk
Online Access:https://doi.org/10.1186/s13148-024-01662-6

Internet

https://doi.org/10.1186/s13148-024-01662-6

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