GAiN: An integrative tool utilizing generative adversarial neural networks for augmented gene expression analysis.

ABSTRACT: Big genomic data and artificial intelligence (AI) are ushering in an era of precision medicine, providing opportunities to study previously under-represented subtypes and rare diseases rather than categorize them as variances. However, clinical researchers face challenges in accessing such novel technologies as well as reliable methods to study small datasets or subcohorts with unique phenotypes. To address this need, we developed an integrative approach, GAiN, to capture patterns of gene expression from small datasets on the basis of an ensemble of generative adversarial networks (GANs) while leveraging big population data. Where conventional biostatistical methods fail, GAiN reliably discovers differentially expressed genes (DEGs) and enriched pathways between two cohorts with limited nu

SUBMITTER: Waters MR 

PROVIDER: S-EPMC10873154 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature