Research Area:  Machine Learning

Abstract:

Breast cancer is a highly aggressive type of cancer with very low median survival. Accurate prognosis prediction of breast cancer can spare a significant number of patients from receiving unnecessary adjuvant systemic treatment and its related expensive medical costs. Previous work relies mostly on selected gene expression data to create a predictive model. The emergence of deep learning methods and multi-dimensional data offers opportunities for more comprehensive analysis of the molecular characteristics of breast cancer and therefore can improve diagnosis, treatment, and prevention. In this study, we propose a Multimodal Deep Neural Network by integrating Multi-dimensional Data (MDNNMD) for the prognosis prediction of breast cancer. The novelty of the method lies in the design of our method-s architecture and the fusion of multi-dimensional data. The comprehensive performance evaluation results show that the proposed method achieves a better performance than the prediction methods with single-dimensional data and other existing approaches. The source code implemented by TensorFlow 1.0 deep learning library can be downloaded from the Github: https://github.com/USTC-HIlab/MDNNMD.

Keywords:  

Author(s) Name:  Dongdong Sun; Minghui Wang; Ao Li

Publisher name:  IEEE

DOI:  10.1109/TCBB.2018.2806438

Volume Information:  ( Volume: 16, Issue: 3, May-June 1 2019)

Paper Link:   https://ieeexplore.ieee.org/abstract/document/8292801