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
	2021
发表期刊	COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
ISSN	2001-0370
卷号	19 页码:2719-2725
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.
关键词	Multi-omics Deep learning Survival analysis Prognosis prediction Interpretable model
DOI	10.1016/j.csbj.2021.04.067
收录类别	SCI
语种	英语
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
WOS类目	Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology
WOS记录号	WOS:000684856500007
出版者	ELSEVIER
引用统计	被引频次：30[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/17242
专题	中国科学院计算技术研究所期刊论文_英文
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.