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

Abstract

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.

Co-expression network analysis identifies a gene signature as a predictive biomarker for energy metabolism in osteosarcoma

“…The OS samples were clustered with 587 energy metabolism-related genes using a non-negative matrix clustering algorithm (NMF) [46]. The "NMF" package in R was applied using the standard "burnet" for 50 iterations, setting the k cluster range from 2 to 10, determining the average contour with the common member matrix, and setting the minimum number of subclass members to ten.…”

Untangling the complexity of multimorbidity with machine learning

“…In addition to hypothesis testing, and going beyond dealing with non-negative values, recent developments in deep learning (DL) have led to improvements in matrix and tensor factorisation methods. Chen et al used an attention mechanism to combine several implementations of NMF for breast cancer prognostication using gene expression and clinical data ( Chen et al, 2019 ). In another study, Schreiber et al proposed a multi-scale deep tensor factorisation for learning latent representations of the human epigenome that shows a promising performance in both imputation and prediction tasks ( Schreiber et al, 2019 ).…”

Deep Learning-Based Prediction Model for Breast Cancer Recurrence Using Adjuvant Breast Cancer Cohort in Tertiary Cancer Center Registry

“…Using recent advances in various machine learning algorithms ( 14 , 15 ), some researchers have worked to develop models that can consider a large amount of complex data, and many efforts are being made to more accurately predict the survival of individual BC patients. The attention-based multi-NMF DNN (AMND) model based on a deep neural network was proposed to predict the survival of BC with the gene expression profile and clinical data of 1,489 patients ( 16 ). The area under the curve (AUC) value of the AMND model was 87.04%.…”