<ul><li>Discovery gene-disease links is important in biology and medicine areas, enabling disease identification and drug repurposing.</li><li>Machine learning approaches accelerate this process by leveraging biological knowledge represented in ontologies and the structure of knowledge graphs.</li><li>A novel framework is introduced for comparing the performance of link prediction versus node-pair classification tasks in gene-disease association prediction.</li><li>Results show that enriching the semantic representation of diseases slightly improves performance, while additional links generate a greater impact. Link prediction methods outperform overall.</li></ul>

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

Discover more