Benchmarking knowledge graph embedding models for the prediction of oligogenic combinations.

ABSTRACT: Identifying the potential oligogenic causes of rare diseases remains a challenge, notwithstanding the advancements made in the last decade. While a variety of predictive and ranking approaches have been proposed, their precision remains limited, as only a small number of high-quality training cases are available and it remains difficult to know which features may be most relevant for the design of new predictors. We hypothesize here that structured biological information, which provides an integration of various relevant biological networks and ontologies in a single heterogeneous knowledge graph, can make a difference as it allows for learning a relevant genetic representation through KGE methods. An exhaustive benchmarking is performed here wherein we assess the performance of various st

SUBMITTER: Bosch I 

PROVIDER: S-EPMC12790627 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature