DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data

Abstract

'Lana X. Garmire'] Multi-omics data are good resources for prognosis and survival prediction; however, these are difficult to integrate computationally. We introduce DeepProg, a novel ensemble framework of deep-learning and machine-learning approaches that robustly predicts patient survival subtypes using multi-omics data. It identifies two optimal survival subtypes in most cancers and yields significantly better risk-stratification than other multi-omics integration methods. DeepProg is highly predictive, exemplified by two liver cancer (C-index 0.73-0.80) and five breast cancer datasets (C-index 0.68-0.73). Pan-cancer analysis associates common genomic signatures in poor survival subtypes with extracellular matrix modeling, immune deregulation, and mitosis processes. DeepProg is freely available at [https://github.com/lanagarmire/DeepProg]() Supplementary Information The online version contains supplementary material available at 10.1186/s13073-021-00930-x.