Multi-domain knowledge graph embeddings for gene-disease association prediction.

ABSTRACT:

Background

Predicting gene-disease associations typically requires exploring diverse sources of information as well as sophisticated computational approaches. Knowledge graph embeddings can help tackle these challenges by creating representations of genes and diseases based on the scientific knowledge described in ontologies, which can then be explored by machine learning algorithms. However, state-of-the-art knowledge graph embeddings are produced over a single ontology or multiple but disconnected ones, ignoring the impact that considering multiple interconnected domains can have on complex tasks such as gene-disease association prediction.

Results

We propose a novel approach to predict gene-disease associations using rich semantic representations based on knowledge graph

SUBMITTER: Nunes S 

PROVIDER: S-EPMC10426189 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature