Random survival forest model identifies novel biomarkers of event-free survival in high-risk pediatric acute lymphoblastic leukemia

High-risk pediatric B-ALL patients experience 5-year negative event rates up to 25%. Although some biomarkers of relapse are utilized in the clinic, their ability to predict outcomes in high-risk patients is limited. Here, we propose a random survival forest (RSF) machine learning model utilizing in...

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Detalles Bibliográficos
Autores principales: Bohannan, Zachary S., Coffman, Frederick, Mitrofanova, Antonina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Research Network of Computational and Structural Biotechnology 2022
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8777142/
https://www.ncbi.nlm.nih.gov/pubmed/35116134
http://dx.doi.org/10.1016/j.csbj.2022.01.003

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8777142/
https://www.ncbi.nlm.nih.gov/pubmed/35116134
http://dx.doi.org/10.1016/j.csbj.2022.01.003

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