{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:34:16Z","timestamp":1772138056496,"version":"3.50.1"},"reference-count":6,"publisher":"Oxford University Press (OUP)","issue":"22","license":[{"start":{"date-parts":[[2022,10,4]],"date-time":"2022-10-04T00:00:00Z","timestamp":1664841600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01CA237170"],"award-info":[{"award-number":["R01CA237170"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"name":"NIH\u2019s National Human Genome Research Institute"},{"DOI":"10.13039\/100000051","name":"NHGRI","doi-asserted-by":"publisher","award":["R01HG010067"],"award-info":[{"award-number":["R01HG010067"]}],"id":[{"id":"10.13039\/100000051","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,11,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>wenda_gpu is available on GitHub at https:\/\/github.com\/greenelab\/wenda_gpu\/.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information<\/jats:title>\n                    <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btac663","type":"journal-article","created":{"date-parts":[[2022,10,3]],"date-time":"2022-10-03T16:09:02Z","timestamp":1664813342000},"page":"5129-5130","source":"Crossref","is-referenced-by-count":0,"title":["wenda_gpu: fast domain adaptation for genomic data"],"prefix":"10.1093","volume":"38","author":[{"given":"Ariel A","family":"Hippen","sequence":"first","affiliation":[{"name":"Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania , Philadelphia, PA 19104, USA"}]},{"given":"Jake","family":"Crawford","sequence":"additional","affiliation":[{"name":"Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania , Philadelphia, PA 19104, USA"}]},{"given":"Jacob R","family":"Gardner","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Science, University of Pennsylvania , Philadelphia, PA 19104, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8713-9213","authenticated-orcid":false,"given":"Casey S","family":"Greene","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus , Aurora, CO 80045, USA"}]}],"member":"286","published-online":{"date-parts":[[2022,10,4]]},"reference":[{"key":"2022112014200349400_btac663-B1","author":"Crawford","year":"2022"},{"key":"2022112014200349400_btac663-B2","article-title":"GPyTorch: blackbox matrix-matrix Gaussian process inference with GPU acceleration","volume":"31","author":"Gardner","year":"2018","journal-title":"Adv. Neural Inf. Process. Syst"},{"key":"2022112014200349400_btac663-B3","doi-asserted-by":"crossref","first-page":"i154","DOI":"10.1093\/bioinformatics\/btz338","article-title":"Weighted elastic net for unsupervised domain adaptation with application to age prediction from DNA methylation data","volume":"35","author":"Handl","year":"2019","journal-title":"Bioinformatics"},{"key":"2022112014200349400_btac663-B4","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.celrep.2018.03.076","article-title":"Genomic and molecular landscape of DNA damage repair deficiency across the cancer genome atlas","volume":"23","author":"Knijnenburg","year":"2018","journal-title":"Cell Rep"},{"key":"2022112014200349400_btac663-B5","doi-asserted-by":"crossref","first-page":"5961","DOI":"10.1038\/s41467-021-26213-y","article-title":"Cancer gene mutation frequencies for the U.S. population","volume":"12","author":"Mendiratta","year":"2021","journal-title":"Nat. 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