Leveraging Deep Learning Techniques and Integrated Omics Data for Tailored Treatment of Breast Cancer.

ABSTRACT: Multiomics data of cancer patients and cell lines, in synergy with deep learning techniques, have aided in unravelling predictive problems related to cancer research and treatment. However, there is still room for improvement in the performance of the existing models based on the aforementioned combination. In this work, we propose two models that complement the treatment of breast cancer patients. First, we discuss our deep learning-based model for breast cancer subtype classification. Second, we propose DCNN-DR, a deep convolute.ion neural network-drug response method for predicting the effectiveness of drugs on in vitro and in vivo breast cancer datasets. Finally, we applied DCNN-DR for predicting effective drugs for the basal-like breast cancer subtype and validated the results with th

SUBMITTER: Khan D 

PROVIDER: S-EPMC9147748 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature