SUPREME: multiomics data integration using graph convolutional networks.

ABSTRACT: To pave the road towards precision medicine in cancer, patients with similar biology ought to be grouped into same cancer subtypes. Utilizing high-dimensional multiomics datasets, integrative approaches have been developed to uncover cancer subtypes. Recently, Graph Neural Networks have been discovered to learn node embeddings utilizing node features and associations on graph-structured data. Some integrative prediction tools have been developed leveraging these advances on multiple networks with some limitations. Addressing these limitations, we developed SUPREME, a node classification framework, which integrates multiple data modalities on graph-structured data. On breast cancer subtyping, unlike existing tools, SUPREME generates patient embeddings from multiple similarity networks utili

SUBMITTER: Kesimoglu ZN 

PROVIDER: S-EPMC10481254 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature