wenda_gpu: fast domain adaptation for genomic data

Motivation Domain adaptation allows for development of predictive models even in cases with limited sample data. Weighted elastic net domain adaptation specifically leverages features of genomic data to maximize transferability but the method is too computationally demanding to apply to many genome-sized datasets. Results We developed wenda\_gpu, which uses GPyTorch to train models on genomic data within hours on a single GPU-enabled machine. We show that wenda\_gpu returns comparable results to the original wenda implementation, and that it can be used for improved prediction of cancer mutation status on small sample sizes than regular elastic net. Availability wenda_gpu is available on GitHub at . Contact casey.s.greene{at}cuanschutz.edu Supplementary information Supplementary data are available at Bioinformatics online. ### Competing Interest Statement The authors have declared no competing interest.