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

Genome Med. 2021 Jul 14;13(1):112. doi: 10.1186/s13073-021-00930-x.


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

Keywords: Cancer; Deep learning; Ensemble learning; Machine learning; Prognosis; Survival; multi-omics.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Algorithms
  • Computational Biology / methods*
  • Databases, Genetic
  • Deep Learning*
  • Female
  • Gene Expression Regulation, Neoplastic
  • Genomics / methods
  • Humans
  • Machine Learning*
  • Models, Theoretical
  • Neoplasms / diagnosis
  • Neoplasms / etiology
  • Neoplasms / metabolism
  • Neoplasms / mortality
  • Prognosis
  • Reproducibility of Results
  • Software*
  • Web Browser

Associated data

  • figshare/10.6084/m9.figshare.14832813