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| Predicting Breast Cancer Survival Length with Multi-Omics Data Fusion |
Huiying Qi1( ),Yuhe Jiang2 |
1School of Health Humanities, Peking University, Beijing 100191, China
2Health Science Center, Peking University, Beijing 100191, China |
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Abstract [Objective] This paper proposes a model using machine learning techniques and various omics data, aiming to better predict the survival length of breast cancer patients. [Methods] The prediction model was established with random forest algorithm. It merged four types of omics data, including gene expression, copy number variation, DNA methylation and protein expression of breast cancer cases from TCGA database. [Results] On the test data set, the model’s prediction precision reached 97.22%, and the recall was 98.13%. Compared with the exisiting models, the AUC value of our new algorithm was the highest (0.8393). [Limitations] The sample size needs to be expanded. [Conclusions] The proposed method is an effective way to predict breast cancer patients’ survival length.
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Received: 07 January 2019
Published: 29 September 2019
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Corresponding Authors:
Huiying Qi
E-mail: qhy@bjmu.edu.cn
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