Clustering rare diseases within an ontology-enriched knowledge graph.

ABSTRACT:

Objective

Identifying sets of rare diseases with shared aspects of etiology and pathophysiology may enable drug repurposing. Toward that aim, we utilized an integrative knowledge graph to construct clusters of rare diseases.

Materials and methods

Data on 3242 rare diseases were extracted from the National Center for Advancing Translational Science Genetic and Rare Diseases Information center internal data resources. The rare disease data enriched with additional biomedical data, including gene and phenotype ontologies, biological pathway data, and small molecule-target activity data, to create a knowledge graph (KG). Node embeddings were trained and clustered. We validated the disease clusters through semantic similarity and feature enrichment analysis.

Results

Thirty

SUBMITTER: Sanjak J 

PROVIDER: S-EPMC10746319 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature