A practical application of generative adversarial networks for RNA-seq analysis to predict the molecular progress of Alzheimer's disease.

ABSTRACT: Next-generation sequencing (NGS) technology has become a powerful tool for dissecting the molecular and pathological signatures of a variety of human diseases. However, the limited availability of biological samples from different disease stages is a major hurdle in studying disease progressions and identifying early pathological changes. Deep learning techniques have recently begun to be applied to analyze NGS data and thereby predict the progression of biological processes. In this study, we applied a deep learning technique called generative adversarial networks (GANs) to predict the molecular progress of Alzheimer's disease (AD). We successfully applied GANs to analyze RNA-seq data from a 5xFAD mouse model of AD, which recapitulates major AD features of massive amyloid-β (Aβ) accumulat

SUBMITTER: Park J 

PROVIDER: S-EPMC7406107 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature