DrugRep-KG: toward learning a unified latent space for drug repurposing using knowledge graphs

Abstract

Drug repurposing or repositioning (DR) refers to finding new therapeutic applications for existing drugs. Current computational DR methods face data representation and negative data sampling challenges. Although retrospective studies attempt to operate various representations, it is a crucial step for an accurate prediction to aggregate these features and bring the associations between drugs and diseases into a unified latent space. In addition, the number of unknown associations between drugs and diseases, which is considered negative data, is much higher than the number of known associations, or positive data, leading to an imbalanced dataset. In this regard, we propose the DrugRep-KG method, which applies a knowledge graph embedding approach for representing drugs and diseases, to address these challenges. Despite the typical DR methods that consider all unknown drug–disease associations as …

Zahra Ghorbanali

Assistant professor

Fatemeh Zare

Associate Professor

My research interests include bioinformatics, computational biology and artificial intelligence.