An integrative deep learning framework for classifying molecular subtypes of breast cancer.

ABSTRACT: Classification of breast cancer subtypes using multi-omics profiles is a difficult problem since the data sets are high-dimensional and highly correlated. Deep neural network (DNN) learning has demonstrated advantages over traditional methods as it does not require any hand-crafted features, but rather automatically extract features from raw data and efficiently analyze high-dimensional and correlated data. We aim to develop an integrative deep learning framework for classifying molecular subtypes of breast cancer. We collect copy number alteration and gene expression data measured on the same breast cancer patients from the Molecular Taxonomy of Breast Cancer International Consortium. We propose a deep learning model to integrate the omics datasets for predicting their molecular subtypes.

SUBMITTER: Mohaiminul Islam M 

PROVIDER: S-EPMC7473884 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature