Wenda_gpu: fast domain adaptation for genomic data.
Ontology highlight
ABSTRACT: Motivation
Domain adaptation allows for the 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 and implementation
wenda_gpu is available on GitHub at https://github.com/greenelab/wenda_gpu/.Supplementary information
Supplementary data are available at Bioinformatics online.
SUBMITTER: Hippen AA
PROVIDER: S-EPMC9665854 | biostudies-literature | 2022 Nov
REPOSITORIES: biostudies-literature
Publications
wenda_gpu: fast domain adaptation for genomic data.
Hippen Ariel A AA Crawford Jake J Gardner Jacob R JR Greene Casey S CS
Bioinformatics (Oxford, England) 20221101 22
<h4>Motivation</h4>Domain adaptation allows for the 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.<h4>Results</h4>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 comp ...[more]
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