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DeepProg: 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...

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Autores principales: Poirion, Olivier B., Jing, Zheng, Chaudhary, Kumardeep, Huang, Sijia, Garmire, Lana X.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8281595/
https://www.ncbi.nlm.nih.gov/pubmed/34261540
http://dx.doi.org/10.1186/s13073-021-00930-x
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author Poirion, Olivier B.
Jing, Zheng
Chaudhary, Kumardeep
Huang, Sijia
Garmire, Lana X.
author_facet Poirion, Olivier B.
Jing, Zheng
Chaudhary, Kumardeep
Huang, Sijia
Garmire, Lana X.
author_sort Poirion, Olivier B.
collection PubMed
description 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 SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13073-021-00930-x.
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spelling pubmed-82815952021-07-16 DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data Poirion, Olivier B. Jing, Zheng Chaudhary, Kumardeep Huang, Sijia Garmire, Lana X. Genome Med Method 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 SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13073-021-00930-x. BioMed Central 2021-07-14 /pmc/articles/PMC8281595/ /pubmed/34261540 http://dx.doi.org/10.1186/s13073-021-00930-x Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Method
Poirion, Olivier B.
Jing, Zheng
Chaudhary, Kumardeep
Huang, Sijia
Garmire, Lana X.
DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data
title DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data
title_full DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data
title_fullStr DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data
title_full_unstemmed DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data
title_short DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data
title_sort deepprog: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8281595/
https://www.ncbi.nlm.nih.gov/pubmed/34261540
http://dx.doi.org/10.1186/s13073-021-00930-x
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