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

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

SUBMITTER: Allesoe RL 

PROVIDER: S-EPMC10017515 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature