GAN-based data augmentation for transcriptomics: survey and comparative assessment.

ABSTRACT:

Motivation

Transcriptomics data are becoming more accessible due to high-throughput and less costly sequencing methods. However, data scarcity prevents exploiting deep learning models' full predictive power for phenotypes prediction. Artificially enhancing the training sets, namely data augmentation, is suggested as a regularization strategy. Data augmentation corresponds to label-invariant transformations of the training set (e.g. geometric transformations on images and syntax parsing on text data). Such transformations are, unfortunately, unknown in the transcriptomic field. Therefore, deep generative models such as generative adversarial networks (GANs) have been proposed to generate additional samples. In this article, we analyze GAN-based data augmentation strategies with resp

SUBMITTER: Lacan A 

PROVIDER: S-EPMC10311334 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature