AIEOP: Associazione Italiana Ematologia Oncologia Pediatrica shared on LinkedIn:
“clinTALL: machine learning-driven multimodal subtype classification and treatment outcome prediction in pediatric T-ALL” – published with Genome Medicine by Springer Nature
AIEOP contributed to this important international study: the first validation cohort comprised of patients enrolled in the AIEOP-BFM ALL 2017 treatment study, supporting research aimed at improving outcomes for children with T-cell acute lymphoblastic leukemia.
clinTALL provides a unified multimodal, multi-task framework that simultaneously delivers subtype classification and event-specific risk prediction in pediatric T-ALL. By offering a user-friendly web interface, clinTALL facilitates the broader application of multi-omics-derived knowledge in clinical research. This framework supports the integration of molecular information into patient management, ultimately contributing to the development of more tailored treatment options and improved treatment outcomes.”
Title: clinTALL: machine learning-driven multimodal subtype classification and treatment outcome prediction in pediatric T-ALL
Authors: Lukas Stoiber, Željko Antić, Stefano Rebellato, Grazia Fazio, Annika Rademacher, Lennart Lenk, Franco Locatelli, Adriana Balduzzi, Gunnar Cario, Carmelo Rizzari, Giovanni Cazzaniga, Jiangyan Yu, Anke Katharina Bergmann
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