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

Journal of Biomedical Semantics

Research

Catia Pesquita¹  Rita T. Sousa¹  Susana Nunes¹ 

[1] LASIGE, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal;

关键词: Ontologies;    Knowledge graph;    Knowledge graph embeddings;    Machine learning;    Gene-disease association prediction;   

DOI  :  10.1186/s13326-023-00291-x

 received in 2022-12-05, accepted in 2023-07-29,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundPredicting 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.ResultsWe propose a novel approach to predict gene-disease associations using rich semantic representations based on knowledge graph embeddings over multiple ontologies linked by logical definitions and compound ontology mappings. The experiments showed that considering richer knowledge graphs significantly improves gene-disease prediction and that different knowledge graph embeddings methods benefit more from distinct types of semantic richness.ConclusionsThis work demonstrated the potential for knowledge graph embeddings across multiple and interconnected biomedical ontologies to support gene-disease prediction. It also paved the way for considering other ontologies or tackling other tasks where multiple perspectives over the data can be beneficial. All software and data are freely available.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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