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Robust pathway-based multi-omics data integration using directed random walks for survival prediction in multiple cancer studies

BACKGROUND: Integrating the rich information from multi-omics data has been a popular approach to survival prediction and bio-marker identification for several cancer studies. To facilitate the integrative analysis of multiple genomic profiles, several studies have suggested utilizing pathway inform...

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Autores principales: Kim, So Yeon, Jeong, Hyun-Hwan, Kim, Jaesik, Moon, Jeong-Hyeon, Sohn, Kyung-Ah
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6489180/
https://www.ncbi.nlm.nih.gov/pubmed/31036036
http://dx.doi.org/10.1186/s13062-019-0239-8
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author Kim, So Yeon
Jeong, Hyun-Hwan
Kim, Jaesik
Moon, Jeong-Hyeon
Sohn, Kyung-Ah
author_facet Kim, So Yeon
Jeong, Hyun-Hwan
Kim, Jaesik
Moon, Jeong-Hyeon
Sohn, Kyung-Ah
author_sort Kim, So Yeon
collection PubMed
description BACKGROUND: Integrating the rich information from multi-omics data has been a popular approach to survival prediction and bio-marker identification for several cancer studies. To facilitate the integrative analysis of multiple genomic profiles, several studies have suggested utilizing pathway information rather than using individual genomic profiles. METHODS: We have recently proposed an integrative directed random walk-based method utilizing pathway information (iDRW) for more robust and effective genomic feature extraction. In this study, we applied iDRW to multiple genomic profiles for two different cancers, and designed a directed gene-gene graph which reflects the interaction between gene expression and copy number data. In the experiments, the performances of the iDRW method and four state-of-the-art pathway-based methods were compared using a survival prediction model which classifies samples into two survival groups. RESULTS: The results show that the integrative analysis guided by pathway information not only improves prediction performance, but also provides better biological insights into the top pathways and genes prioritized by the model in both the neuroblastoma and the breast cancer datasets. The pathways and genes selected by the iDRW method were shown to be related to the corresponding cancers. CONCLUSIONS: In this study, we demonstrated the effectiveness of a directed random walk-based multi-omics data integration method applied to gene expression and copy number data for both breast cancer and neuroblastoma datasets. We revamped a directed gene-gene graph considering the impact of copy number variation on gene expression and redefined the weight initialization and gene-scoring method. The benchmark result for iDRW with four pathway-based methods demonstrated that the iDRW method improved survival prediction performance and jointly identified cancer-related pathways and genes for two different cancer datasets. REVIEWERS: This article was reviewed by Helena Molina-Abril and Marta Hidalgo.
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spelling pubmed-64891802019-06-05 Robust pathway-based multi-omics data integration using directed random walks for survival prediction in multiple cancer studies Kim, So Yeon Jeong, Hyun-Hwan Kim, Jaesik Moon, Jeong-Hyeon Sohn, Kyung-Ah Biol Direct Research BACKGROUND: Integrating the rich information from multi-omics data has been a popular approach to survival prediction and bio-marker identification for several cancer studies. To facilitate the integrative analysis of multiple genomic profiles, several studies have suggested utilizing pathway information rather than using individual genomic profiles. METHODS: We have recently proposed an integrative directed random walk-based method utilizing pathway information (iDRW) for more robust and effective genomic feature extraction. In this study, we applied iDRW to multiple genomic profiles for two different cancers, and designed a directed gene-gene graph which reflects the interaction between gene expression and copy number data. In the experiments, the performances of the iDRW method and four state-of-the-art pathway-based methods were compared using a survival prediction model which classifies samples into two survival groups. RESULTS: The results show that the integrative analysis guided by pathway information not only improves prediction performance, but also provides better biological insights into the top pathways and genes prioritized by the model in both the neuroblastoma and the breast cancer datasets. The pathways and genes selected by the iDRW method were shown to be related to the corresponding cancers. CONCLUSIONS: In this study, we demonstrated the effectiveness of a directed random walk-based multi-omics data integration method applied to gene expression and copy number data for both breast cancer and neuroblastoma datasets. We revamped a directed gene-gene graph considering the impact of copy number variation on gene expression and redefined the weight initialization and gene-scoring method. The benchmark result for iDRW with four pathway-based methods demonstrated that the iDRW method improved survival prediction performance and jointly identified cancer-related pathways and genes for two different cancer datasets. REVIEWERS: This article was reviewed by Helena Molina-Abril and Marta Hidalgo. BioMed Central 2019-04-29 /pmc/articles/PMC6489180/ /pubmed/31036036 http://dx.doi.org/10.1186/s13062-019-0239-8 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Kim, So Yeon
Jeong, Hyun-Hwan
Kim, Jaesik
Moon, Jeong-Hyeon
Sohn, Kyung-Ah
Robust pathway-based multi-omics data integration using directed random walks for survival prediction in multiple cancer studies
title Robust pathway-based multi-omics data integration using directed random walks for survival prediction in multiple cancer studies
title_full Robust pathway-based multi-omics data integration using directed random walks for survival prediction in multiple cancer studies
title_fullStr Robust pathway-based multi-omics data integration using directed random walks for survival prediction in multiple cancer studies
title_full_unstemmed Robust pathway-based multi-omics data integration using directed random walks for survival prediction in multiple cancer studies
title_short Robust pathway-based multi-omics data integration using directed random walks for survival prediction in multiple cancer studies
title_sort robust pathway-based multi-omics data integration using directed random walks for survival prediction in multiple cancer studies
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6489180/
https://www.ncbi.nlm.nih.gov/pubmed/31036036
http://dx.doi.org/10.1186/s13062-019-0239-8
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