Prediction of Breast Cancer Treatment–Induced Fatigue by Machine Learning Using Genome-Wide Association Data

BACKGROUND: We aimed at predicting fatigue after breast cancer treatment using machine learning on clinical covariates and germline genome-wide data. METHODS: We accessed germline genome-wide data of 2799 early-stage breast cancer patients from the Cancer Toxicity study (NCT01993498). The primary en...

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Detalles Bibliográficos
Autores principales: Lee, Sangkyu, Deasy, Joseph O, Oh, Jung Hun, Di Meglio, Antonio, Dumas, Agnes, Menvielle, Gwenn, Charles, Cecile, Boyault, Sandrine, Rousseau, Marina, Besse, Celine, Thomas, Emilie, Boland, Anne, Cottu, Paul, Tredan, Olivier, Levy, Christelle, Martin, Anne-Laure, Everhard, Sibille, Ganz, Patricia A, Partridge, Ann H, Michiels, Stefan, Deleuze, Jean-François, Andre, Fabrice, Vaz-Luis, Ines
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7583150/
https://www.ncbi.nlm.nih.gov/pubmed/33490863
http://dx.doi.org/10.1093/jncics/pkaa039

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7583150/
https://www.ncbi.nlm.nih.gov/pubmed/33490863
http://dx.doi.org/10.1093/jncics/pkaa039

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