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.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.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.doi http://dx.doi.org/10.1038/s41587-022-01520-x
• dc.identifier.issn 1087-0156
• dc.identifier.uri http://hdl.handle.net/10230/56088
• 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.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Data integration
• dc.subject.keyword Machine learning
• dc.subject.keyword Systems biology
• dc.subject.keyword Type 2 diabetes
• dc.title Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion