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MOGONET integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification
To fully utilize the advances in omics technologies and achieve a more comprehensive understanding of human diseases, novel computational methods are required for integrative analysis of multiple types of omics data. Here, we present a novel multi-omics integrative method named Multi-Omics Graph cOn...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8187432/ https://www.ncbi.nlm.nih.gov/pubmed/34103512 http://dx.doi.org/10.1038/s41467-021-23774-w |
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author | Wang, Tongxin Shao, Wei Huang, Zhi Tang, Haixu Zhang, Jie Ding, Zhengming Huang, Kun |
author_facet | Wang, Tongxin Shao, Wei Huang, Zhi Tang, Haixu Zhang, Jie Ding, Zhengming Huang, Kun |
author_sort | Wang, Tongxin |
collection | PubMed |
description | To fully utilize the advances in omics technologies and achieve a more comprehensive understanding of human diseases, novel computational methods are required for integrative analysis of multiple types of omics data. Here, we present a novel multi-omics integrative method named Multi-Omics Graph cOnvolutional NETworks (MOGONET) for biomedical classification. MOGONET jointly explores omics-specific learning and cross-omics correlation learning for effective multi-omics data classification. We demonstrate that MOGONET outperforms other state-of-the-art supervised multi-omics integrative analysis approaches from different biomedical classification applications using mRNA expression data, DNA methylation data, and microRNA expression data. Furthermore, MOGONET can identify important biomarkers from different omics data types related to the investigated biomedical problems. |
format | Online Article Text |
id | pubmed-8187432 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-81874322021-07-01 MOGONET integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification Wang, Tongxin Shao, Wei Huang, Zhi Tang, Haixu Zhang, Jie Ding, Zhengming Huang, Kun Nat Commun Article To fully utilize the advances in omics technologies and achieve a more comprehensive understanding of human diseases, novel computational methods are required for integrative analysis of multiple types of omics data. Here, we present a novel multi-omics integrative method named Multi-Omics Graph cOnvolutional NETworks (MOGONET) for biomedical classification. MOGONET jointly explores omics-specific learning and cross-omics correlation learning for effective multi-omics data classification. We demonstrate that MOGONET outperforms other state-of-the-art supervised multi-omics integrative analysis approaches from different biomedical classification applications using mRNA expression data, DNA methylation data, and microRNA expression data. Furthermore, MOGONET can identify important biomarkers from different omics data types related to the investigated biomedical problems. Nature Publishing Group UK 2021-06-08 /pmc/articles/PMC8187432/ /pubmed/34103512 http://dx.doi.org/10.1038/s41467-021-23774-w Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Wang, Tongxin Shao, Wei Huang, Zhi Tang, Haixu Zhang, Jie Ding, Zhengming Huang, Kun MOGONET integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification |
title | MOGONET integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification |
title_full | MOGONET integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification |
title_fullStr | MOGONET integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification |
title_full_unstemmed | MOGONET integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification |
title_short | MOGONET integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification |
title_sort | mogonet integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8187432/ https://www.ncbi.nlm.nih.gov/pubmed/34103512 http://dx.doi.org/10.1038/s41467-021-23774-w |
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