期刊
GENOME MEDICINE
卷 13, 期 1, 页码 -出版社
BMC
DOI: 10.1186/s13073-021-00930-x
关键词
Survival; Prognosis; multi-omics; Cancer; Ensemble learning; Deep learning; Machine learning
资金
- NIEHS by the trans-NIH Big Data to Knowledge (BD2K) initiative [K01ES025434]
- NIH/NIGMS [P20 COBRE GM103457]
- NLM [R01 LM012373, R01 LM012907]
- NICHD [R01 HD084633]
DeepProg is a framework that integrates deep-learning and machine-learning approaches for predicting patient survival subtypes using multi-omics data. It identifies two optimal survival subtypes in most cancers and offers better risk-stratification than other methods.
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
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