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Attention-Based Multi-NMF Deep Neural Network with Multimodality Data for Breast Cancer Prognosis Model

Today, it has become a hot issue in cancer research to make precise prognostic prediction for breast cancer patients, which can not only effectively avoid overtreatment and medical resources waste, but also provide scientific basis to help medical staff and patients family members to make right medi...

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Detalles Bibliográficos
Autores principales: Chen, Hongling, Gao, Mingyan, Zhang, Ying, Liang, Wenbin, Zou, Xianchun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6535865/
https://www.ncbi.nlm.nih.gov/pubmed/31214619
http://dx.doi.org/10.1155/2019/9523719
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author Chen, Hongling
Gao, Mingyan
Zhang, Ying
Liang, Wenbin
Zou, Xianchun
author_facet Chen, Hongling
Gao, Mingyan
Zhang, Ying
Liang, Wenbin
Zou, Xianchun
author_sort Chen, Hongling
collection PubMed
description Today, it has become a hot issue in cancer research to make precise prognostic prediction for breast cancer patients, which can not only effectively avoid overtreatment and medical resources waste, but also provide scientific basis to help medical staff and patients family members to make right medical decisions. As well known, cancer is a partly inherited disease with various important biological markers, especially the gene expression profile data and clinical data. Therefore, the accuracy of prediction model can be improved by integrating gene expression profile data and clinical data. In this paper, we proposed an end-to-end model, Attention-based Multi-NMF DNN (AMND), which combines clinical data and gene expression data extracted by Multiple Nonnegative Matrix Factorization algorithms (Multi-NMF) for the prognostic prediction of breast cancer. The innovation of this method is highlighted through using clinical data and combining multiple feature selection methods with the help of Attention mechanism. The results of comprehensive performance evaluation show that the proposed model reports better predictive performances than either models only using data of single modality, e.g., gene or clinical, or models based on any single NMF improved methods which only use one of the NMF algorithms to extract features. The performance of our model is competitive or even better than other previously reported models. Meanwhile, AMND can be extended to the survival prediction of other cancer diseases, providing a new strategy for breast cancer prognostic prediction.
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spelling pubmed-65358652019-06-18 Attention-Based Multi-NMF Deep Neural Network with Multimodality Data for Breast Cancer Prognosis Model Chen, Hongling Gao, Mingyan Zhang, Ying Liang, Wenbin Zou, Xianchun Biomed Res Int Research Article Today, it has become a hot issue in cancer research to make precise prognostic prediction for breast cancer patients, which can not only effectively avoid overtreatment and medical resources waste, but also provide scientific basis to help medical staff and patients family members to make right medical decisions. As well known, cancer is a partly inherited disease with various important biological markers, especially the gene expression profile data and clinical data. Therefore, the accuracy of prediction model can be improved by integrating gene expression profile data and clinical data. In this paper, we proposed an end-to-end model, Attention-based Multi-NMF DNN (AMND), which combines clinical data and gene expression data extracted by Multiple Nonnegative Matrix Factorization algorithms (Multi-NMF) for the prognostic prediction of breast cancer. The innovation of this method is highlighted through using clinical data and combining multiple feature selection methods with the help of Attention mechanism. The results of comprehensive performance evaluation show that the proposed model reports better predictive performances than either models only using data of single modality, e.g., gene or clinical, or models based on any single NMF improved methods which only use one of the NMF algorithms to extract features. The performance of our model is competitive or even better than other previously reported models. Meanwhile, AMND can be extended to the survival prediction of other cancer diseases, providing a new strategy for breast cancer prognostic prediction. Hindawi 2019-05-13 /pmc/articles/PMC6535865/ /pubmed/31214619 http://dx.doi.org/10.1155/2019/9523719 Text en Copyright © 2019 Hongling Chen et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Chen, Hongling
Gao, Mingyan
Zhang, Ying
Liang, Wenbin
Zou, Xianchun
Attention-Based Multi-NMF Deep Neural Network with Multimodality Data for Breast Cancer Prognosis Model
title Attention-Based Multi-NMF Deep Neural Network with Multimodality Data for Breast Cancer Prognosis Model
title_full Attention-Based Multi-NMF Deep Neural Network with Multimodality Data for Breast Cancer Prognosis Model
title_fullStr Attention-Based Multi-NMF Deep Neural Network with Multimodality Data for Breast Cancer Prognosis Model
title_full_unstemmed Attention-Based Multi-NMF Deep Neural Network with Multimodality Data for Breast Cancer Prognosis Model
title_short Attention-Based Multi-NMF Deep Neural Network with Multimodality Data for Breast Cancer Prognosis Model
title_sort attention-based multi-nmf deep neural network with multimodality data for breast cancer prognosis model
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6535865/
https://www.ncbi.nlm.nih.gov/pubmed/31214619
http://dx.doi.org/10.1155/2019/9523719
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