Integrating Somatic Mutations for Breast Cancer Survival Prediction Using Machine Learning Methods.

ABSTRACT: Breast cancer is the most common malignancy in women, and because it has a high mortality rate, it is urgent to develop computational methods to increase the accuracy of breast cancer survival predictive models. Although multi-omics data such as gene expression have been extensively used in recent studies, the accurate prognosis of breast cancer remains a challenge. Somatic mutations are another important and promising data source for studying cancer development, and its effect on the prognosis of breast cancer remains to be further explored. Meanwhile, these omics datasets are high-dimensional and redundant. Therefore, we adopted multiple kernel learning (MKL) to efficiently integrate somatic mutation to currently molecular data including gene expression, copy number variation (CNV), meth

SUBMITTER: He Z 

PROVIDER: S-EPMC7848170 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature