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Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models

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dc.contributor.author Allesøe, Rosa Lundbye
dc.contributor.author IMI DIRECT Consortium
dc.date.accessioned 2023-03-08T07:20:29Z
dc.date.available 2023-03-08T07:20:29Z
dc.date.issued 2023
dc.identifier.citation Allesøe RL, Lundgaard AT, Hernández Medina R, Aguayo-Orozco A, Johansen J, Nissen JN, et al. Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models. Nat Biotechnol. 2023 Mar;41(3):399-408. DOI: 10.1038/s41587-022-01520-x
dc.identifier.issn 1087-0156
dc.identifier.uri http://hdl.handle.net/10230/56088
dc.description.abstract The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. However, the scale and heterogeneous nature of multi-modal data makes integration and inference a non-trivial task. We developed a deep-learning-based framework, multi-omics variational autoencoders (MOVE), to integrate such data and applied it to a cohort of 789 people with newly diagnosed type 2 diabetes with deep multi-omics phenotyping from the DIRECT consortium. Using in silico perturbations, we identified drug-omics associations across the multi-modal datasets for the 20 most prevalent drugs given to people with type 2 diabetes with substantially higher sensitivity than univariate statistical tests. From these, we among others, identified novel associations between metformin and the gut microbiota as well as opposite molecular responses for the two statins, simvastatin and atorvastatin. We used the associations to quantify drug-drug similarities, assess the degree of polypharmacy and conclude that drug effects are distributed across the multi-omics modalities.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Nature Research
dc.relation.ispartof Nat Biotechnol. 2023 Mar;41(3):399-408
dc.rights © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
dc.rights.uri http://creativecommons.org/licenses/by/4.0/
dc.title Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.1038/s41587-022-01520-x
dc.subject.keyword Data integration
dc.subject.keyword Machine learning
dc.subject.keyword Systems biology
dc.subject.keyword Type 2 diabetes
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion

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