Conversely, treatment with one or more drugs and the associated polypharmacy effects can have considerable impact on the molecular profile of the individual however, such changes are still largely unknown 3. Multiple organs and confounders are typically involved including comorbidities and polypharmacy 1, 2. 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.ĭrug-response patterns in individuals with complex disease, such as type 2 diabetes (T2D), are intricate. 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. 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. 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. However, the scale and heterogeneous nature of multi-modal data makes integration and inference a non-trivial task. The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. Nature Biotechnology volume 41, pages 399–408 ( 2023) Cite this article This flexibility delivers custom program configurations that reduce setup and on-going maintenance.Discovery of drug–omics associations in type 2 diabetes with generative deep-learning models
For example, rule changes can be created and assigned at various levels.
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