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

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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Bibliographic Details
Main Authors: Yi-Wen Hsiao, Chun-Liang Tao, Eric Y. Chuang, Tzu-Pin Lu
Format: Article
Language:English
Published: Elsevier 2021-05-01
Series:Journal of Advanced Research
Subjects:
Ovarian cancer
Risk prediction
Gene expression
Machine learning
GA-XGBoost
Bagging algorithm
Online Access:http://www.sciencedirect.com/science/article/pii/S2090123220302320

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http://www.sciencedirect.com/science/article/pii/S2090123220302320

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