biostudies-literature001520Poirion OBNational Institute of Environmental Health SciencesNICHD NIH HHSNIEHS NIH HHSU.S. National Library of MedicineNLM NIH HHSNational Institutes of HealthNational Institute of General Medical SciencesNIGMS NIH HHS112https://www.ebi.ac.uk/biostudies/studies/S-EPMC8281595biostudies-literatureUnknown13(1)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.Genome medicineDeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data.PMC8281595LM012373HD084633P20 GM103457R01 HD084633K01 ES025434GM103457LM012907K01ES025434R01 LM012373R01 LM012907Huang SJing ZChaudhary KGarmire LXPoirion OB152falseDeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data.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.2021-01-01T00:00:00Z2021 Jul2024-11-12T14:53:08.882Z2022-02-10T20:26:09.392ZS-EPMC82815953426154010.1186/s13073-021-00930-x