Self-supervised graph representation learning integrates multiple molecular networks and decodes gene-disease relationships.

ABSTRACT: Leveraging molecular networks to discover disease-relevant modules is a long-standing challenge. With the accumulation of interactomes, there is a pressing need for powerful computational approaches to handle the inevitable noise and context-specific nature of biological networks. Here, we introduce Graphene, a two-step self-supervised representation learning framework tailored to concisely integrate multiple molecular networks and adapted to gene functional analysis via downstream re-training. In practice, we first leverage GNN (graph neural network) pre-training techniques to obtain initial node embeddings followed by re-training Graphene using a graph attention architecture, achieving superior performance over competing methods for pathway gene recovery, disease gene reprioritization, a

SUBMITTER: Wang Y 

PROVIDER: S-EPMC9868676 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature