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

被引:25
作者
Chen, Hongling [1 ]
Gao, Mingyan [1 ]
Zhang, Ying [1 ]
Liang, Wenbin [2 ]
Zou, Xianchun [1 ]
机构
[1] Southwest Univ, Coll Comp & Informat Sci, Chongqing 400715, Peoples R China
[2] Southwest Univ, Coll Chem & Chem Engn, Minist Educ, Key Lab Luminescent & Real Time Analyt Chem, Chongqing 400715, Peoples R China
关键词
CLASSIFICATION; EXPRESSION;
D O I
10.1155/2019/9523719
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
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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页数:11
相关论文
共 31 条
[1]  
[Anonymous], P INT C BIOM ENG INF
[2]  
[Anonymous], NATURE
[3]  
[Anonymous], IEEE T COMPUTATIONAL
[4]  
[Anonymous], 2010, GENOME BIOL, DOI DOI 10.1186/gb-2010-11-10-r106
[5]  
[Anonymous], 2016, P IEEE INT C MACH LE
[6]  
[Anonymous], 2014, Neural Information Processing Systems
[7]  
Bahdanau D., NEURAL MACHINE TRANS
[8]   PROBABILISTIC NON-NEGATIVE MATRIX FACTORIZATION: THEORY AND APPLICATION TO MICROARRAY DATA ANALYSIS [J].
Bayar, Belhassen ;
Bouaynaya, Nidhal ;
Shterenberg, Roman .
JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY, 2014, 12 (01)
[9]   SVD based initialization: A head start for nonnegative matrix factorization [J].
Boutsidis, C. ;
Gallopoulos, E. .
PATTERN RECOGNITION, 2008, 41 (04) :1350-1362
[10]  
Chorowski Jan, 2015, COMPUTER ENCE, V10, P429