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

ABSTRACT:

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 models used in OC have low sensitivity, and few of them are able to identify OC patients at high risk of mortality, which would both optimize the treatment of high-risk patients and prevent unnecessary medical intervention in those at low risk.

Objectives

To this end, we have developed a bagging-based algorithm with GA-XGBoost models that predicts the risk of death from OC using gene expression profiles.

Methods

Four gene expression datasets from public sources were used as t

SUBMITTER: Hsiao YW 

PROVIDER: S-EPMC8132202 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature