Journal Article
. 2019 Jun; 2019:9523719.
doi: 10.1155/2019/9523719.

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

Hongling Chen 1 Mingyan Gao 1 Ying Zhang 1 Wenbin Liang 2 Xianchun Zou 1 
  • PMID: 31214619
  •     20 References
  •     4 citations


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.

Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.
T R Golub, D K Slonim, +9 authors, E S Lander.
Science, 1999 Oct 16; 286(5439). PMID: 10521349
Highly Cited.
Learning the parts of objects by non-negative matrix factorization.
D D Lee, H S Seung.
Nature, 1999 Nov 05; 401(6755). PMID: 10548103
Highly Cited.
A gene-expression signature as a predictor of survival in breast cancer.
Marc J van de Vijver, Yudong D He, +18 authors, René Bernards.
N Engl J Med, 2002 Dec 20; 347(25). PMID: 12490681
Highly Cited.
Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks.
Olivier Gevaert, Frank De Smet, +2 authors, Bart De Moor.
Bioinformatics, 2006 Jul 29; 22(14). PMID: 16873470
Improved breast cancer prognosis through the combination of clinical and genetic markers.
Yijun Sun, Steve Goodison, +2 authors, William Farmerie.
Bioinformatics, 2006 Nov 30; 23(1). PMID: 17130137    Free PMC article.
Projected gradient methods for nonnegative matrix factorization.
Chih-Jen Lin.
Neural Comput, 2007 Aug 25; 19(10). PMID: 17716011
Breast cancer prognostic classification in the molecular era: the role of histological grade.
Emad A Rakha, Jorge S Reis-Filho, +15 authors, Ian O Ellis.
Breast Cancer Res, 2010 Sep 02; 12(4). PMID: 20804570    Free PMC article.
Highly Cited. Review.
Differential expression analysis for sequence count data.
Simon Anders, Wolfgang Huber.
Genome Biol, 2010 Oct 29; 11(10). PMID: 20979621    Free PMC article.
Highly Cited.
Molecular and cellular heterogeneity in breast cancer: challenges for personalized medicine.
Ashley G Rivenbark, Siobhan M O'Connor, William B Coleman.
Am J Pathol, 2013 Sep 03; 183(4). PMID: 23993780    Free PMC article.
Probabilistic non-negative matrix factorization: theory and application to microarray data analysis.
Belhassen Bayar, Nidhal Bouaynaya, Roman Shterenberg.
J Bioinform Comput Biol, 2014 Jan 29; 12(1). PMID: 24467759
Hessian regularization based non-negative matrix factorization for gene expression data clustering.
Xiao Liu, Jun Shi, Congzhi Wang.
Annu Int Conf IEEE Eng Med Biol Soc, 2016 Jan 07; 2015. PMID: 26737203
Personalized treatment of women with early breast cancer: a risk-group specific cost-effectiveness analysis of adjuvant chemotherapy accounting for companion prognostic tests OncotypeDX and Adjuvant!Online.
Beate Jahn, Ursula Rochau, +9 authors, Uwe Siebert.
BMC Cancer, 2017 Oct 19; 17(1). PMID: 29037213    Free PMC article.
Support vector machine for breast cancer classification using diffusion-weighted MRI histogram features: Preliminary study.
Igor Vidić, Liv Egnell, +6 authors, Pål Erik Goa.
J Magn Reson Imaging, 2017 Oct 19; 47(5). PMID: 29044896
Cost analysis of breast cancer diagnostic assessment programs.
G N Honein-AbouHaidar, J S Hoch, +3 authors, A R Gagliardi.
Curr Oncol, 2017 Nov 02; 24(5). PMID: 29089805    Free PMC article.
Association between gene expression profile of the primary tumor and chemotherapy response of metastatic breast cancer.
Cemile Dilara Savci-Heijink, Hans Halfwerk, Jan Koster, Marc Joan Van de Vijver.
BMC Cancer, 2017 Nov 15; 17(1). PMID: 29132326    Free PMC article.
Prognostic value of PAM50 and risk of recurrence score in patients with early-stage breast cancer with long-term follow-up.
Hege O Ohnstad, Elin Borgen, +11 authors, Bjørn Naume.
Breast Cancer Res, 2017 Nov 16; 19(1). PMID: 29137653    Free PMC article.
Breast Cancer Cell Line Classification and Its Relevance with Breast Tumor Subtyping.
Xiaofeng Dai, Hongye Cheng, Zhonghu Bai, Jia Li.
J Cancer, 2017 Nov 22; 8(16). PMID: 29158785    Free PMC article.
Highly Cited. Review.
Classification of Genes Based on Age-Related Differential Expression in Breast Cancer.
Gunhee Lee, Minho Lee.
Genomics Inform, 2018 Jan 09; 15(4). PMID: 29307142    Free PMC article.
A multimodal deep neural network for human breast cancer prognosis prediction by integrating multi-dimensional data.
Dongdong Sun, Minghui Wang, Ao Li.
IEEE/ACM Trans Comput Biol Bioinform, 2018 Jul 12;. PMID: 29994639
Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.
Freddie Bray, Jacques Ferlay, +3 authors, Ahmedin Jemal.
CA Cancer J Clin, 2018 Sep 13; 68(6). PMID: 30207593
Highly Cited.
Co-expression network analysis identifies a gene signature as a predictive biomarker for energy metabolism in osteosarcoma.
Naiqiang Zhu, Jingyi Hou, +3 authors, Bin Chen.
Cancer Cell Int, 2020 Jun 26; 20. PMID: 32581649    Free PMC article.
Untangling the complexity of multimorbidity with machine learning.
Abdelaali Hassaine, Gholamreza Salimi-Khorshidi, Dexter Canoy, Kazem Rahimi.
Mech Ageing Dev, 2020 Aug 10; 190. PMID: 32768443    Free PMC article.
Identification and analysis of consensus RNA motifs binding to the genome regulator CTCF.
Shuzhen Kuang, Liangjiang Wang.
NAR Genom Bioinform, 2021 Feb 13; 2(2). PMID: 33575587    Free PMC article.
Deep Learning-Based Prediction Model for Breast Cancer Recurrence Using Adjuvant Breast Cancer Cohort in Tertiary Cancer Center Registry.
Ji-Yeon Kim, Yong Seok Lee, +10 authors, Young-Hyuck Im.
Front Oncol, 2021 May 22; 11. PMID: 34017679    Free PMC article.