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 and/or platform based therapeutic development. Toward that aim, we utilized an integrative knowledge graph-based approach to constructing clusters of rare diseases.

Materials and methods

Data on 3,242 rare diseases were extracted from the National Center for Advancing Translational Science (NCATS) Genetic and Rare Diseases Information center (GARD) internal data resources. The rare disease data was 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 used to convert nodes into vectors upon which k-means cl

SUBMITTER: Sanjak J 

PROVIDER: S-EPMC9949046 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature