A Deep-Learning Model With the Attention Mechanism Could Rigorously Predict Survivals in Neuroblastoma

doi:10.3389/fonc.2021.653863

CASIA OpenIR > 复杂系统管理与控制国家重点实验室 > 影像分析与机器视觉

	A Deep-Learning Model With the Attention Mechanism Could Rigorously Predict Survivals in Neuroblastoma
	Feng, Chenzhao 1; Xiang, Tianyu2,3 ; Yi, Zixuan 4; Meng, Xinyao 1; Chu, Xufeng 5; Huang, Guiyang 5; Zhao, Xiang 1; Chen, Feng 6; Xiong, Bo 5; Feng, Jiexiong 1
发表期刊	FRONTIERS IN ONCOLOGY
ISSN	2234-943X
	2021-07-14
卷号	11 页码:14
通讯作者	Chen, Feng(cfeng3000@163.com) ; Xiong, Bo(bxiong@hust.edu.cn) ; Feng, Jiexiong(fengjiexiong@126.com)
摘要	Background: Neuroblastoma is one of the most devastating forms of childhood cancer. Despite large amounts of attempts in precise survival prediction in neuroblastoma, the prediction efficacy remains to be improved. Methods: Here, we applied a deep-learning (DL) model with the attention mechanism to predict survivals in neuroblastoma. We utilized 2 groups of features separated from 172 genes, to train 2 deep neural networks and combined them by the attention mechanism. Results: This classifier could accurately predict survivals, with areas under the curve of receiver operating characteristic (ROC) curves and time-dependent ROC reaching 0.968 and 0.974 in the training set respectively. The accuracy of the model was further confirmed in a validation cohort. Importantly, the two feature groups were mapped to two groups of patients, which were prognostic in Kaplan-Meier curves. Biological analyses showed that they exhibited diverse molecular backgrounds which could be linked to the prognosis of the patients. Conclusions: In this study, we applied artificial intelligence methods to improve the accuracy of neuroblastoma survival prediction based on gene expression and provide explanations for better understanding of the molecular mechanisms underlying neuroblastoma.
关键词	neuroblastoma survival deep-learning (DL) individual therapy transcriptome
DOI	10.3389/fonc.2021.653863
关键词[WOS]	RISK CLASSIFICATION ; OUTCOME PREDICTION ; NONCODING RNAS ; CANCER ; EXPRESSION ; STAT3
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2016YFE0203900]
项目资助者	National Key Research and Development Program of China
WOS研究方向	Oncology
WOS类目	Oncology
WOS记录号	WOS:000680681600001
出版者	FRONTIERS MEDIA SA
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/45626
专题	复杂系统管理与控制国家重点实验室_影像分析与机器视觉
通讯作者	Chen, Feng; Xiong, Bo; Feng, Jiexiong
作者单位	1.Huazhong Univ Sci & Technol, Tongji Hosp, Tongji Med Coll, Dept Pediat Surg, Wuhan, Peoples R China 2.Tongji Univ, Coll Elect & Informat Engn, Dept Control Sci & Engn, Shanghai, Peoples R China 3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China 4.Wuhan Univ, Coll Arts & Sci, Sch Math & Stat, Wuhan, Peoples R China 5.Huazhong Univ Sci & Technol, Tongji Med Coll, Dept Forens Med, Wuhan, Peoples R China 6.Fujian Med Univ, Union Hosp, Dept Pediat Surg, Fuzhou, Peoples R China
推荐引用方式 GB/T 7714	Feng, Chenzhao,Xiang, Tianyu,Yi, Zixuan,et al. A Deep-Learning Model With the Attention Mechanism Could Rigorously Predict Survivals in Neuroblastoma[J]. FRONTIERS IN ONCOLOGY,2021,11:14.
APA	Feng, Chenzhao.,Xiang, Tianyu.,Yi, Zixuan.,Meng, Xinyao.,Chu, Xufeng.,...&Feng, Jiexiong.(2021).A Deep-Learning Model With the Attention Mechanism Could Rigorously Predict Survivals in Neuroblastoma.FRONTIERS IN ONCOLOGY,11,14.
MLA	Feng, Chenzhao,et al."A Deep-Learning Model With the Attention Mechanism Could Rigorously Predict Survivals in Neuroblastoma".FRONTIERS IN ONCOLOGY 11(2021):14.