Breast Cancer Prediction Using Gated Attentive Multimodal Deep Learning

Women are prone to breast cancer, which is a major cause of death. One out of every eight women has a lifetime risk of developing this cancer. Early diagnosis of this disease is critical and enhances the success rate of cure. It is extremely important to determine which genes are associated with the disease. However, too many features make studies on gene data challenging. In this study, an attention-based multimodal deep learning model was created by combining data from clinical, copy number alteration and gene expression sources. Based on our findings, it was observed that the proposed method produced successful results and will be helpful in breast cancer prediction tasks.