Please use this identifier to cite or link to this item: https://doi.org/10.1186/s12859-021-04261-x
Title: Machine learning-based prediction of survival prognosis in cervical cancer
Authors: Ding, Dongyan
Lang, Tingyuan
Zou, Dongling
Tan, Jiawei
Chen, Jia
Zhou, Lei 
Wang, Dong
Li, Rong
Li, Yunzhe
Liu, Jingshu
Ma, Cui
Zhou, Qi
Keywords: Cervical cancer
Machine learning
miRNAs
Support-vector machines
Survival prediction
Issue Date: 16-Jun-2021
Publisher: BioMed Central Ltd
Citation: Ding, Dongyan, Lang, Tingyuan, Zou, Dongling, Tan, Jiawei, Chen, Jia, Zhou, Lei, Wang, Dong, Li, Rong, Li, Yunzhe, Liu, Jingshu, Ma, Cui, Zhou, Qi (2021-06-16). Machine learning-based prediction of survival prognosis in cervical cancer. BMC Bioinformatics 22 (1) : 331. ScholarBank@NUS Repository. https://doi.org/10.1186/s12859-021-04261-x
Rights: Attribution 4.0 International
Abstract: Background: Accurately forecasting the prognosis could improve cervical cancer management, however, the currently used clinical features are difficult to provide enough information. The aim of this study is to improve forecasting capability by developing a miRNAs-based machine learning survival prediction model. Results: The expression characteristics of miRNAs were chosen as features for model development. The cervical cancer miRNA expression data was obtained from The Cancer Genome Atlas database. Preprocessing, including unquantified data removal, missing value imputation, samples normalization, log transformation, and feature scaling, was performed. In total, 42 survival-related miRNAs were identified by Cox Proportional-Hazards analysis. The patients were optimally clustered into four groups with three different 5-years survival outcome (? 90%, ? 65%, ? 40%) by K-means clustering algorithm base on top 10 survival-related miRNAs. According to the K-means clustering result, a prediction model with high performance was established. The pathways analysis indicated that the miRNAs used play roles involved in the regulation of cancer stem cells. Conclusion: A miRNAs-based machine learning cervical cancer survival prediction model was developed that robustly stratifies cervical cancer patients into high survival rate (5-years survival rate ? 90%), moderate survival rate (5-years survival rate ? 65%), and low survival rate (5-years survival rate ? 40%). © 2021, The Author(s).
Source Title: BMC Bioinformatics
URI: https://scholarbank.nus.edu.sg/handle/10635/233045
ISSN: 1471-2105
DOI: 10.1186/s12859-021-04261-x
Rights: Attribution 4.0 International
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