HELICAL was first and foremost a training programme: the ESRs have been equipped with skills that put them to the forefront of an area of expertise that is critical to Europe’s continued success and prosperity, namely clinically meaningful application of informatics skills to link clinical and research data, to bring advances in data science and artificial intelligence to bear on the health of European citizens.
Examples of research progress beyond the state of the art include:
• Development of novel data science approaches for modelling the impact of weather and pollution on occurrence of systemic vasculitis (Kawasaki disease) in Japan
• A tantalising association between sulphur dioxide pollution and occurrence of vasculitis
• Innovative software that links an individual's location to prevalent weather and pollution conditions to facilitate exploration at scale of these conditions on occurrence of disease
• The first epigenetic profiles in specific white cell subsets from patients with vasculitis
• Linkage of genetic signals in the blood vessel wall of patients with vasculitis to disease pattern and severity
• Comprehensive optimisation of isolation of the tiny particles that break off from cells in the body, providing opportunities for generation of novel biomarkers for use in the clinic
• Development of a new artificial intelligence imaging approach to analysing kidney tissue
The action has had significant impact in the field of rare disease research in Europe as it has high visibility and traction in the vasculitis field. Stimulated by explicit links with the European Reference Network for rare immune disorders (ERN-RITA) and the European Vasculitis Society, several HELICAL initiatives have begun to highlight and ameliorate the challenges of innovation using health data from rare disease patients. For example, two EU projects have been built upon the outputs of HELICAL: the FAIRVASC project (
https://fairvasc.eu/(s’ouvre dans une nouvelle fenêtre)) which seeks to create a semantic web platform that supports distributed learning from different regional registries across Europe and the PARADISE (Personalisation of relapse risk in autoimmunity) consortium which builds applies the autoantibody and transcriptomic signatures discovered in WP3 to help build a predictive algorithm to quantify relapse risk.
