Applying multimodal AI to physiological waveforms improves genetic prediction of cardiovascular traits.

ABSTRACT: Electronic health records, biobanks, and wearable biosensors enable the collection of multiple health modalities from many individuals. Access to multimodal health data provides a unique opportunity for genetic studies of complex traits because different modalities relevant to a single physiological system (e.g., circulatory system) encode complementary and overlapping information. We propose a multimodal deep learning method, multimodal representation learning for genetic discovery on low-dimensional embeddings (M-REGLE), for discovering genetic associations from a joint representation of complementary electrophysiological waveform modalities. M-REGLE jointly learns a lower representation (i.e., latent factors) of multimodal physiological waveforms using a convolutional variational autoen

SUBMITTER: Zhou Y 

PROVIDER: S-EPMC12256885 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature