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.

Bibliographic Details
Main Authors:	Tongxin Wang, Wei Shao, Zhi Huang, Haixu Tang, Jie Zhang, Zhengming Ding, Kun Huang
Format:	Article
Language:	English
Published:	Nature Publishing Group 2021-06-01
Series:	Nature Communications
Online Access:	https://doi.org/10.1038/s41467-021-23774-w

id	doaj-288469ae1b8548578e585c76f18caf41
record_format	Article
spelling	doaj-288469ae1b8548578e585c76f18caf412021-06-13T11:16:10ZengNature Publishing GroupNature Communications2041-17232021-06-0112111310.1038/s41467-021-23774-wMOGONET integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identificationTongxin Wang0Wei Shao1Zhi Huang2Haixu Tang3Jie Zhang4Zhengming Ding5Kun Huang6Department of Computer Science, Indiana University BloomingtonDepartment of Medicine, Indiana University School of MedicineDepartment of Medicine, Indiana University School of MedicineDepartment of Computer Science, Indiana University BloomingtonDepartment of Medical and Molecular Genetics, Indiana University School of MedicineDepartment of Computer Science, Tulane UniversityDepartment of Medicine, Indiana University School of MedicineOur 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.https://doi.org/10.1038/s41467-021-23774-w
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Tongxin Wang Wei Shao Zhi Huang Haixu Tang Jie Zhang Zhengming Ding Kun Huang
spellingShingle	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 Nature Communications
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 Publishing Group
series	Nature Communications
issn	2041-1723
publishDate	2021-06-01
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.
url	https://doi.org/10.1038/s41467-021-23774-w
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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