A risk prediction model of gene signatures in ovarian cancer through bagging of GA-XGBoost models

INTRODUCTION: Ovarian cancer (OC) is one of the most frequent gynecologic cancers among women, and high-accuracy risk prediction techniques are essential to effectively select the best intervention strategies and clinical management for OC patients at different risk levels. Current risk prediction m...

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Detalles Bibliográficos
Autores principales: Hsiao, Yi-Wen, Tao, Chun-Liang, Chuang, Eric Y., Lu, Tzu-Pin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Medicine
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8132202/
https://www.ncbi.nlm.nih.gov/pubmed/34026291
http://dx.doi.org/10.1016/j.jare.2020.11.006

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8132202/
https://www.ncbi.nlm.nih.gov/pubmed/34026291
http://dx.doi.org/10.1016/j.jare.2020.11.006

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