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

文献类型：期刊论文

作者Feng, Chenzhao1; Xiang, Tianyu2,3; Yi, Zixuan4; Meng, Xinyao1; Chu, Xufeng5; Huang, Guiyang5; Zhao, Xiang1; Chen, Feng6; Xiong, Bo5; Feng, Jiexiong1
刊名FRONTIERS IN ONCOLOGY
出版日期2021-07-14
卷号11页码:14
ISSN号2234-943X
关键词neuroblastoma survival deep-learning (DL) individual therapy transcriptome
DOI10.3389/fonc.2021.653863
通讯作者Chen, Feng(cfeng3000@163.com) ; Xiong, Bo(bxiong@hust.edu.cn) ; Feng, Jiexiong(fengjiexiong@126.com)
英文摘要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.
WOS关键词RISK CLASSIFICATION ; OUTCOME PREDICTION ; NONCODING RNAS ; CANCER ; EXPRESSION ; STAT3
资助项目National Key Research and Development Program of China[2016YFE0203900]
WOS研究方向Oncology
语种英语
出版者FRONTIERS MEDIA SA
WOS记录号WOS:000680681600001
资助机构National Key Research and Development Program of China
源URL[http://ir.ia.ac.cn/handle/173211/45625]  
专题自动化研究所_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团队
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.

入库方式： OAI收割

来源：自动化研究所

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