Prediction of Breast Cancer Treatment\textendashInduced Fatigue by Machine Learning Using Genome-Wide Association Data

• Published in: JNCI cancer spectrum Vol. 4; no. 5
• Main Authors: Lee, Sangkyu, Deasy, Joseph O, Oh, Jung Hun, Di Meglio, Antonio, Dumas, Agnès, 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
• Format: Journal Article
• Language: English
• Published: Oxford University Press 01.10.2020
• Subjects: Life Sciences

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 endp...