Group Lasso Regularized Deep Learning for Cancer Prognosis from Multi-Omics and Clinical Features.

ABSTRACT: Accurate prognosis of patients with cancer is important for the stratification of patients, the optimization of treatment strategies, and the design of clinical trials. Both clinical features and molecular data can be used for this purpose, for instance, to predict the survival of patients censored at specific time points. Multi-omics data, including genome-wide gene expression, methylation, protein expression, copy number alteration, and somatic mutation data, are becoming increasingly common in cancer studies. To harness the rich information in multi-omics data, we developed GDP (Group lass regularized Deep learning for cancer Prognosis), a computational tool for survival prediction using both clinical and multi-omics data. GDP integrated a deep learning framework and Cox proportional ha

SUBMITTER: Xie G 

PROVIDER: S-EPMC6471789 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature