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

Journal: Bioinformatics (Oxford, England)
PMID: 40217132

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.

Authors

  • Chang-Uk Jeong
    Department of Software and Computer Engineering, Ajou University, Suwon, 16499, South Korea.
  • Jaesik Kim
    Department of Computer Engineering, Ajou University, Suwon, South Korea.
  • Dokyoon Kim
    Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, USA.
  • Kyung-Ah Sohn
    Department of Artificial Intelligence, Ajou University, Suwon, South Korea.

Keywords

External Resources

Popular Topics
Recent Journals