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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2019
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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. |
format | Online Article Text |
id | pubmed-6535865 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
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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