Machine learning applications for prediction of relapse in childhood acute lymphoblastic leukemia

The prediction of relapse in childhood acute lymphoblastic leukemia (ALL) is a critical factor for successful treatment and follow-up planning. Our goal was to construct an ALL relapse prediction model based on machine learning algorithms. Monte Carlo cross-validation nested by 10-fold cross-validat...

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Detalles Bibliográficos
Autores principales: Pan, Liyan, Liu, Guangjian, Lin, Fangqin, Zhong, Shuling, Xia, Huimin, Sun, Xin, Liang, Huiying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2017
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5547099/
https://www.ncbi.nlm.nih.gov/pubmed/28784991
http://dx.doi.org/10.1038/s41598-017-07408-0

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5547099/
https://www.ncbi.nlm.nih.gov/pubmed/28784991
http://dx.doi.org/10.1038/s41598-017-07408-0

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