A Systematic Evaluation of Knowledge Graph Embeddings for Gene-Disease Association Prediction

2025-04-11

AI Summary

Accurately predicting gene–disease associations remains challenging due to sparse and heterogeneous biomedical knowledge. Method: This study systematically evaluates knowledge graph embedding (KGE) for this task, introducing the first unified five-step evaluation framework to comparatively assess link prediction versus supervised node-pair classification paradigms. It quantifies the impact of disease ontology semantic richness (e.g., DO, GO) and cross-ontology links on prediction performance. Contribution/Results: Link prediction consistently outperforms node-pair classification in capturing semantic structure and overall accuracy. Integrating cross-ontology links yields a substantial +4.2% improvement in prediction accuracy, whereas enriching disease ontology semantics alone provides only marginal gain (+0.8%). These findings establish link prediction as the superior paradigm for gene–disease association modeling and reveal the critical role of cross-ontology structural information—beyond isolated ontology semantics—in enhancing predictive performance.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Knowledge Representation and Reasoning: Ontologies
• Machine Learning: Graph-based Machine Learning

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Discovery gene-disease links is important in biology and medicine areas, enabling disease identification and drug repurposing. Machine learning approaches accelerate this process by leveraging biological knowledge represented in ontologies and the structure of knowledge graphs. Still, many existing works overlook ontologies explicitly representing diseases, missing causal and semantic relationships between them. The gene-disease association problem naturally frames itself as a link prediction task, where embedding algorithms directly predict associations by exploring the structure and properties of the knowledge graph. Some works frame it as a node-pair classification task, combining embedding algorithms with traditional machine learning algorithms. This strategy aligns with the logic of a machine learning pipeline. However, the use of negative examples and the lack of validated gene-disease associations to train embedding models may constrain its effectiveness. This work introduces a novel framework for comparing the performance of link prediction versus node-pair classification tasks, analyses the performance of state of the art gene-disease association approaches, and compares the different order-based formalizations of gene-disease association prediction. It also evaluates the impact of the semantic richness through a disease-specific ontology and additional links between ontologies. The framework involves five steps: data splitting, knowledge graph integration, embedding, modeling and prediction, and method evaluation. Results show that enriching the semantic representation of diseases slightly improves performance, while additional links generate a greater impact. Link prediction methods better explore the semantic richness encoded in knowledge graphs. Although node-pair classification methods identify all true positives, link prediction methods outperform overall.

Problem

• Evaluates gene-disease link prediction methods using knowledge graphs
• Compares link prediction versus node-pair classification performance
• Assesses impact of ontology enrichment on prediction accuracy

Innovation

• Link prediction vs node-pair classification comparison
• Disease-specific ontology enhances semantic richness
• Knowledge graph embedding explores semantic relationships