DeepOmix: A scalable and interpretable multi-omics deep learning framework and application in cancer survival analysis

doi:10.1016/j.csbj.2021.04.067

	DeepOmix: A scalable and interpretable multi-omics deep learning framework and application in cancer survival analysis
	Zhao, Lianhe 1,2; Dong, Qiongye 1; Luo, Chunlong 1,2; Wu, Yang 1; Bu, Dechao 1; Qi, Xiaoning 1,2; Luo, Yufan 1,2; Zhao, Yi 1,3
刊名	COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
	2021
卷号	19 页码:2719-2725
关键词	Multi-omics Deep learning Survival analysis Prognosis prediction Interpretable model
ISSN号	2001-0370
DOI	10.1016/j.csbj.2021.04.067
英文摘要	Integrative analysis of multi-omics data can elucidate valuable insights into complex molecular mechanisms for various diseases. However, due to their different modalities and high dimension, utilizing and integrating different types of omics data suffers from great challenges. There is an urgent need to develop a powerful method to improve survival prediction and detect functional gene modules from multi-omics data. To deal with these problems, we present DeepOmix (a scalable and interpretable multi-Omics Deep learning framework and application in cancer survival analysis), a flexible, scalable, and interpretable method for extracting relationships between the clinical survival time and multi-omics data based on a deep learning framework. DeepOmix enables the non-linear combination of variables from different omics datasets and incorporates prior biological information defined by users (such as signaling pathways and tissue networks). Benchmark experiments demonstrate that DeepOmix outperforms the other five cutting-edge prediction methods. Besides, Lower Grade Glioma (LGG) is taken as the case study to perform the prognosis prediction and illustrate the functional module nodes which are associated with the prognostic result in the prediction model. (C) 2021 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.
资助项目	National Key R&D Program of China[2019YFC1709801] ; Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology[JBZX-202003] ; National Natural Science Foundation of China[32070670] ; Zhejiang Provincial Natural Science Foundation of China[LY21C060003] ; Zhejiang Provincial Natural Science Foundation of China[LY20C060001]
WOS研究方向	Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000684856500007
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/17241]
专题	中国科学院计算技术研究所
通讯作者	Zhao, Yi
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Adv Comp Res Ctr, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Univ Chinese Acad Sci, Hwa Mei Hosp, Ningbo 315000, Peoples R China
推荐引用方式 GB/T 7714	Zhao, Lianhe,Dong, Qiongye,Luo, Chunlong,et al. DeepOmix: A scalable and interpretable multi-omics deep learning framework and application in cancer survival analysis[J]. COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL,2021,19:2719-2725.
APA	Zhao, Lianhe.,Dong, Qiongye.,Luo, Chunlong.,Wu, Yang.,Bu, Dechao.,...&Zhao, Yi.(2021).DeepOmix: A scalable and interpretable multi-omics deep learning framework and application in cancer survival analysis.COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL,19,2719-2725.
MLA	Zhao, Lianhe,et al."DeepOmix: A scalable and interpretable multi-omics deep learning framework and application in cancer survival analysis".COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL 19(2021):2719-2725.