Multi-ontology embeddings approach on human-aligned multi-ontologies representation for gene-disease associations prediction.

ABSTRACT:

Objectives

Knowledge graphs and ontologies in the biomedical domain provide rich contextual knowledge for a variety of challenges. Employing that for knowledge-driven NLP tasks such as gene-disease association prediction represents a promising way to increase the predictive power of a model.

Methods

We investigated the power of infusing the embedding of two aligned ontologies as prior knowledge to the NLP models. We evaluated the performance of different models on some large-scale gene-disease association datasets and compared it with a model without incorporating contextualized knowledge (BERT).

Results

The experiments demonstrated that the knowledge-infused model slightly outperforms BERT by creating a small number of bridges. Thus, indicating that incorporating cro

SUBMITTER: Wang Y 

PROVIDER: S-EPMC10651438 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature