Cargando…
A Deep-Learning Model With the Attention Mechanism Could Rigorously Predict Survivals in Neuroblastoma
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...
Autores principales: | , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8317851/ https://www.ncbi.nlm.nih.gov/pubmed/34336652 http://dx.doi.org/10.3389/fonc.2021.653863 |
_version_ | 1783730133058715648 |
---|---|
author | Feng, Chenzhao Xiang, Tianyu Yi, Zixuan Meng, Xinyao Chu, Xufeng Huang, Guiyang Zhao, Xiang Chen, Feng Xiong, Bo Feng, Jiexiong |
author_facet | Feng, Chenzhao Xiang, Tianyu Yi, Zixuan Meng, Xinyao Chu, Xufeng Huang, Guiyang Zhao, Xiang Chen, Feng Xiong, Bo Feng, Jiexiong |
author_sort | Feng, Chenzhao |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8317851 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-83178512021-07-29 A Deep-Learning Model With the Attention Mechanism Could Rigorously Predict Survivals in Neuroblastoma Feng, Chenzhao Xiang, Tianyu Yi, Zixuan Meng, Xinyao Chu, Xufeng Huang, Guiyang Zhao, Xiang Chen, Feng Xiong, Bo Feng, Jiexiong Front Oncol Oncology 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. Frontiers Media S.A. 2021-07-14 /pmc/articles/PMC8317851/ /pubmed/34336652 http://dx.doi.org/10.3389/fonc.2021.653863 Text en Copyright © 2021 Feng, Xiang, Yi, Meng, Chu, Huang, Zhao, Chen, Xiong and Feng https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Oncology Feng, Chenzhao Xiang, Tianyu Yi, Zixuan Meng, Xinyao Chu, Xufeng Huang, Guiyang Zhao, Xiang Chen, Feng Xiong, Bo Feng, Jiexiong A Deep-Learning Model With the Attention Mechanism Could Rigorously Predict Survivals in Neuroblastoma |
title | A Deep-Learning Model With the Attention Mechanism Could Rigorously Predict Survivals in Neuroblastoma |
title_full | A Deep-Learning Model With the Attention Mechanism Could Rigorously Predict Survivals in Neuroblastoma |
title_fullStr | A Deep-Learning Model With the Attention Mechanism Could Rigorously Predict Survivals in Neuroblastoma |
title_full_unstemmed | A Deep-Learning Model With the Attention Mechanism Could Rigorously Predict Survivals in Neuroblastoma |
title_short | A Deep-Learning Model With the Attention Mechanism Could Rigorously Predict Survivals in Neuroblastoma |
title_sort | deep-learning model with the attention mechanism could rigorously predict survivals in neuroblastoma |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8317851/ https://www.ncbi.nlm.nih.gov/pubmed/34336652 http://dx.doi.org/10.3389/fonc.2021.653863 |
work_keys_str_mv | AT fengchenzhao adeeplearningmodelwiththeattentionmechanismcouldrigorouslypredictsurvivalsinneuroblastoma AT xiangtianyu adeeplearningmodelwiththeattentionmechanismcouldrigorouslypredictsurvivalsinneuroblastoma AT yizixuan adeeplearningmodelwiththeattentionmechanismcouldrigorouslypredictsurvivalsinneuroblastoma AT mengxinyao adeeplearningmodelwiththeattentionmechanismcouldrigorouslypredictsurvivalsinneuroblastoma AT chuxufeng adeeplearningmodelwiththeattentionmechanismcouldrigorouslypredictsurvivalsinneuroblastoma AT huangguiyang adeeplearningmodelwiththeattentionmechanismcouldrigorouslypredictsurvivalsinneuroblastoma AT zhaoxiang adeeplearningmodelwiththeattentionmechanismcouldrigorouslypredictsurvivalsinneuroblastoma AT chenfeng adeeplearningmodelwiththeattentionmechanismcouldrigorouslypredictsurvivalsinneuroblastoma AT xiongbo adeeplearningmodelwiththeattentionmechanismcouldrigorouslypredictsurvivalsinneuroblastoma AT fengjiexiong adeeplearningmodelwiththeattentionmechanismcouldrigorouslypredictsurvivalsinneuroblastoma AT fengchenzhao deeplearningmodelwiththeattentionmechanismcouldrigorouslypredictsurvivalsinneuroblastoma AT xiangtianyu deeplearningmodelwiththeattentionmechanismcouldrigorouslypredictsurvivalsinneuroblastoma AT yizixuan deeplearningmodelwiththeattentionmechanismcouldrigorouslypredictsurvivalsinneuroblastoma AT mengxinyao deeplearningmodelwiththeattentionmechanismcouldrigorouslypredictsurvivalsinneuroblastoma AT chuxufeng deeplearningmodelwiththeattentionmechanismcouldrigorouslypredictsurvivalsinneuroblastoma AT huangguiyang deeplearningmodelwiththeattentionmechanismcouldrigorouslypredictsurvivalsinneuroblastoma AT zhaoxiang deeplearningmodelwiththeattentionmechanismcouldrigorouslypredictsurvivalsinneuroblastoma AT chenfeng deeplearningmodelwiththeattentionmechanismcouldrigorouslypredictsurvivalsinneuroblastoma AT xiongbo deeplearningmodelwiththeattentionmechanismcouldrigorouslypredictsurvivalsinneuroblastoma AT fengjiexiong deeplearningmodelwiththeattentionmechanismcouldrigorouslypredictsurvivalsinneuroblastoma |