Abstract

In recent years, knowledge graphs have emerged as a powerful tool for organising and analysing complex and heterogeneous biomedical data. Treatments for rare diseases, which are themselves often poorly understood and difficult to diagnose, can benefit greatly from the application of graph ML. By integrating data from a variety of sources, including electronic health records, genetic data, and scientific literature, knowledge graphs can help researchers identify patterns and relationships that may be relevant to understanding and treating rare diseases. In this presentation, we will explore the current state of knowledge graph applications in rare disease research, including examples of successful implementations and the challenges that remain. We will also discuss the potential of knowledge graphs to accelerate the pace of rare disease research and improve patient outcomes, as well as opportunities for future research in this area.