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

被引：0
作者：
Olivier B. Poirion
Zheng Jing
Kumardeep Chaudhary
Sijia Huang
Lana X. Garmire
机构：
[1] The Jackson Laboratory,Current address: Computational Sciences
[2] University of Hawaii Cancer Center,Current address: Department of Computational Medicine and Bioinformatics
[3] University of Michigan,Current address: Department of Genetics and Genomic Sciences
[4] Icahn School of Medicine at Mount Sinai,Current address: Department of Biostatistics, Epidemiology and Informatics
[5] University of Pennsylvania,undefined
来源：
Genome Medicine | / 13卷
关键词：
Survival; Prognosis; multi-omics; Cancer; Ensemble learning; Deep learning; Machine learning;
D O I：
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学科分类号：
摘要：
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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