MOGONET integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification

Our understanding of human disease can be improved by integrating the abundance of high throughput biomedical data. Here, the authors use deep learning methods successfully used on images to integrate various types of omics data to improve patient classification and identify disease biomarkers.

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Autores principales:	Tongxin Wang, Wei Shao, Zhi Huang, Haixu Tang, Jie Zhang, Zhengming Ding, Kun Huang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/288469ae1b8548578e585c76f18caf41
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id	oai:doaj.org-article:288469ae1b8548578e585c76f18caf41
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spelling	oai:doaj.org-article:288469ae1b8548578e585c76f18caf412021-12-02T17:34:29ZMOGONET integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification10.1038/s41467-021-23774-w2041-1723https://doaj.org/article/288469ae1b8548578e585c76f18caf412021-06-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-23774-whttps://doaj.org/toc/2041-1723Our understanding of human disease can be improved by integrating the abundance of high throughput biomedical data. Here, the authors use deep learning methods successfully used on images to integrate various types of omics data to improve patient classification and identify disease biomarkers.Tongxin WangWei ShaoZhi HuangHaixu TangJie ZhangZhengming DingKun HuangNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Tongxin Wang Wei Shao Zhi Huang Haixu Tang Jie Zhang Zhengming Ding Kun Huang MOGONET integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification
description	Our understanding of human disease can be improved by integrating the abundance of high throughput biomedical data. Here, the authors use deep learning methods successfully used on images to integrate various types of omics data to improve patient classification and identify disease biomarkers.
format	article
author	Tongxin Wang Wei Shao Zhi Huang Haixu Tang Jie Zhang Zhengming Ding Kun Huang
author_facet	Tongxin Wang Wei Shao Zhi Huang Haixu Tang Jie Zhang Zhengming Ding Kun Huang
author_sort	Tongxin Wang
title	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_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_sort	mogonet integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/288469ae1b8548578e585c76f18caf41
work_keys_str_mv	AT tongxinwang mogonetintegratesmultiomicsdatausinggraphconvolutionalnetworksallowingpatientclassificationandbiomarkeridentification AT weishao mogonetintegratesmultiomicsdatausinggraphconvolutionalnetworksallowingpatientclassificationandbiomarkeridentification AT zhihuang mogonetintegratesmultiomicsdatausinggraphconvolutionalnetworksallowingpatientclassificationandbiomarkeridentification AT haixutang mogonetintegratesmultiomicsdatausinggraphconvolutionalnetworksallowingpatientclassificationandbiomarkeridentification AT jiezhang mogonetintegratesmultiomicsdatausinggraphconvolutionalnetworksallowingpatientclassificationandbiomarkeridentification AT zhengmingding mogonetintegratesmultiomicsdatausinggraphconvolutionalnetworksallowingpatientclassificationandbiomarkeridentification AT kunhuang mogonetintegratesmultiomicsdatausinggraphconvolutionalnetworksallowingpatientclassificationandbiomarkeridentification
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MOGONET integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification

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