Combining Neuroimaging and Omics Datasets for Disease Classification Using Graph Neural Networks.

ABSTRACT: Both neuroimaging and genomics datasets are often gathered for the detection of neurodegenerative diseases. Huge dimensionalities of neuroimaging data as well as omics data pose tremendous challenge for methods integrating multiple modalities. There are few existing solutions that can combine both multi-modal imaging and multi-omics datasets to derive neurological insights. We propose a deep neural network architecture that combines both structural and functional connectome data with multi-omics data for disease classification. A graph convolution layer is used to model functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) data simultaneously to learn compact representations of the connectome. A separate set of graph convolution layers are then used to model multi

SUBMITTER: Chan YH 

PROVIDER: S-EPMC9168232 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature