GPDBN: deep bilinear network integrating both genomic data and pathological images for breast cancer prognosis prediction.

ABSTRACT: Breast cancer is a very heterogeneous disease and there is an urgent need to design computational methods that can accurately predict the prognosis of breast cancer for appropriate therapeutic regime. Recently, deep learning-based methods have achieved great success in prognosis prediction, but many of them directly combine features from different modalities that may ignore the complex inter-modality relations. In addition, existing deep learning-based methods do not take intra-modality relations into consideration that are also beneficial to prognosis prediction. Therefore, it is of great importance to develop a deep learning-based method that can take advantage of the complementary information between intra-modality and inter-modality by integrating data from different modalities for mor

SUBMITTER: Wang Z 

PROVIDER: S-EPMC8479662 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature