Applying a GAN-based classifier to improve transcriptome-based prognostication in breast cancer.

ABSTRACT: Established prognostic tests based on limited numbers of transcripts can identify high-risk breast cancer patients, yet are approved only for individuals presenting with specific clinical features or disease characteristics. Deep learning algorithms could hold potential for stratifying patient cohorts based on full transcriptome data, yet the development of robust classifiers is hampered by the number of variables in omics datasets typically far exceeding the number of patients. To overcome this hurdle, we propose a classifier based on a data augmentation pipeline consisting of a Wasserstein generative adversarial network (GAN) with gradient penalty and an embedded auxiliary classifier to obtain a trained GAN discriminator (T-GAN-D). Applied to 1244 patients of the METABRIC breast cancer c

SUBMITTER: Gutta C 

PROVIDER: S-EPMC10101642 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature