DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data
dc.contributor.author | Poirion, Olivier B. | |
dc.contributor.author | Jing, Zheng | |
dc.contributor.author | Chaudhary, Kumardeep | |
dc.contributor.author | Huang, Sijia | |
dc.contributor.author | Garmire, Lana X. | |
dc.date.accessioned | 2022-08-10T18:39:57Z | |
dc.date.available | 2022-08-10T18:39:57Z | |
dc.date.issued | 2021-07-14 | |
dc.identifier.citation | Genome Medicine. 2021 Jul 14;13(1):112 | |
dc.identifier.uri | https://doi.org/10.1186/s13073-021-00930-x | |
dc.identifier.uri | https://hdl.handle.net/2027.42/173896 | en |
dc.description.abstract | Abstract 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 | |
dc.title | DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data | |
dc.type | Journal Article | |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/173896/1/13073_2021_Article_930.pdf | |
dc.identifier.doi | https://dx.doi.org/10.7302/5627 | |
dc.language.rfc3066 | en | |
dc.rights.holder | The Author(s) | |
dc.date.updated | 2022-08-10T18:39:55Z | |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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