GeOKG: geometry-aware knowledge graph embedding for Gene Ontology and genes.

ABSTRACT:

Motivation

Leveraging deep learning for the representation learning of Gene Ontology (GO) and Gene Ontology Annotation (GOA) holds significant promise for enhancing downstream biological tasks such as protein-protein interaction prediction. Prior approaches have predominantly used text- and graph-based methods, embedding GO and GOA in a single geometric space (e.g. Euclidean or hyperbolic). However, since the GO graph exhibits a complex and nonmonotonic hierarchy, single-space embeddings are insufficient to fully capture its structural nuances.

Results

In this study, we address this limitation by exploiting geometric interaction to better reflect the intricate hierarchical structure of GO. Our proposed method, Geometry-Aware Knowledge Graph Embeddings for GO and Genes (GeOKG

SUBMITTER: Jeong CU 

PROVIDER: S-EPMC12036960 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature